Heliyon. 2023 Feb 25;9(3):e14007. doi: 10.1016/j.heliyon.2023.e14007. eCollection 2023 Mar.

ABSTRACT

Cellular senescence is a cell fate that prominently impacts physiological and pathophysiological processes. Diverse cellular stresses induce it, and dramatic gene expression changes accompany it. However, determining the interactions comprising the gene regulatory network (GRN) governing senescence remains challenging. Recent advances in signal processing techniques provide opportunities to reconstruct GRNs. Here, we describe a GRN for senescence integrating time-series transcriptome and transcription factor depletion datasets. Specifically, we infer a set of differential equations using the "Sparse Identification of Nonlinear Dynamics" (SINDy) algorithm, discriminate genes with potential hidden regulators, validate the inferred GRN for time-points not included in the training data, and comprehensively benchmark our approach. Our work is a proof of concept for a data-driven GRN reconstruction method, consolidating an iterative, powerful mathematical platform for senescence modeling that can be used to test hypotheses in silico and has the potential for future discoveries of clinical impact.

PMID:36938415 | PMC:PMC10015196 | DOI:10.1016/j.heliyon.2023.e14007

Front Plant Sci. 2023 Mar 2;14:1074935. doi: 10.3389/fpls.2023.1074935. eCollection 2023.

ABSTRACT

Ipomoeapes-caprae (L.) (IPC) is a common species in tropical and subtropical coastal areas and one of the world's most widely distributed plants. It has attracted researchers for its outstanding biological, ecological and medicinal values. It has been reported that the genetic diversity of IPCs located on different continents is very low because of their frequent gene flow. During the long journey of evolution, every aspect of the plant morphologies has evolved to the best adaptivity to the environment, seeking their survival and progeny expansion. However, the fundamental genetic characteristics of IPC and how their seed adapted to the success of population expansion remain unknown. In this study, the fundamental genetic characteristics, including the genome size and the chromosome number of IPC, were investigated. The results showed that IPC's genome size is approximately 0.98-1.08 GB, and the chromosome number is 2n=30, providing the basic information for further genome analysis. In order to decipher the long-distance dispersal secret of this species, the fruit and seed developments, seed morphology, and seed germination were extensively investigated and described. The results showed an exquisite adaptive mechanism of IPC seeds to fulfil the population expansion via ocean currents. The large cavity inside the seeds and the dense tomenta on the surface provide the buoyancy force for the seeds to float on the seawater. The hard seed coats significantly obstructed the water absorption, thus preventing the seed from germination during the dispersal. Meanwhile, the fully developed embryos of IPC also have physiological dormancy. The physical and physiological characteristics of IPC seeds provide insight into the mechanism of their long-distance dispersal across the oceans. Moreover, based on morphological observation and semi-section microscopy, the development pattern of IPC glander trichomes was described, and their physiological functions were also discussed.

PMID:36938054 | PMC:PMC10017971 | DOI:10.3389/fpls.2023.1074935

Front Plant Sci. 2023 Mar 1;14:1123644. doi: 10.3389/fpls.2023.1123644. eCollection 2023.

ABSTRACT

Peanut (Arachis hypogaea L.), one of the leading oilseed crops worldwide, is an important source of vegetable oil, protein, minerals and vitamins. Peanut is widely cultivated in Asia, Africa and America, and China is the largest producer and consumer of peanut. Genetic engineering has shown great potential to alter the DNA makeup of an organism which is largely hindered by the low transformation and screening efficiency including in peanut. DsRed2 is a reporter gene widely utilized in genetic transformation to facilitate the screening of transformants, but never used in peanut genetic transformation. In this study, we have demonstrated the potential of the red fluorescence protein DsRed2 as a visual reporter to improve screening efficiency in peanut. DsRed2 was firstly expressed in protoplasts isolated from peanut cultivar Zhonhua 12 by PEG, and red fluorescence was successfully detected. Then, DsRed2 was expressed in peanut plants Zhonghua 12 driven by 35S promoter via Agrobacterium tumefaciens-mediated transformation. Red fluorescence was visually observed in calli and regenerated shoots, as well as in roots, leaves, flowers, fresh pod shells and mature seeds, suggesting that transgenic screening could be initiated at the early stage of transformation, and continued to the progeny. Upon screening with DsRed2, the positive plant rate was increased from 56.9% to 100%. The transgenic line was then used as the male parent to be crossed with Zhonghua 24, and the hybrid seeds showed red fluorescence as well, indicating that DsRed2 could be applied to hybrid plant identification very efficiently. DsRed2 was also expressed in hairy roots of Huayu 23 via Agrobacterium rhizogenes-mediated transformation, and the transgenic roots were easily selected by red fluorescence. In summary, the DsRed2 is an ideal reporter to achieve maximum screening efficiency and accuracy in peanut genetic transformation.

PMID:36938000 | PMC:PMC10014910 | DOI:10.3389/fpls.2023.1123644

Quant Biol. 2022 Dec;10(4):307-320.

ABSTRACT

BACKGROUND: Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects.

RESULTS: Here, we provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing different types of CRISPR screen data. We also discuss mechanisms and underlying challenges for the analysis of dropout screens, sorting-based screens and single-cell screens.

CONCLUSION: Different analysis approaches should be chosen based on the design of screens. This review will help community to better design novel algorithms and provide suggestions for wet-lab researchers to choose from different analysis methods.

PMID:36937794 | PMC:PMC10019185

Front Microbiol. 2023 Mar 2;14:1143770. doi: 10.3389/fmicb.2023.1143770. eCollection 2023.

ABSTRACT

A metabolic model, representing all biochemical reactions in a cell, is a prerequisite for several approaches in systems biology used to explore the metabolic phenotype of an organism. Despite the use of Euglena in diverse industrial applications and as a biological model, there is limited understanding of its metabolic network capacity. The unavailability of the completed genome data and the highly complex evolution of Euglena are significant obstacles to the reconstruction and analysis of its genome-scale metabolic model. In this mini-review, we discuss the current state and challenges of metabolic network reconstruction in Euglena gracilis. We have collated and present the available relevant data for the metabolic network reconstruction of E. gracilis, which could be used to improve the quality of the metabolic model of E. gracilis. Furthermore, we deliver the potential applications of the model in metabolic engineering. Altogether, it is supposed that this mini-review would facilitate the investigation of metabolic networks in Euglena and further lay out a direction for model-assisted metabolic engineering.

PMID:36937274 | PMC:PMC10018167 | DOI:10.3389/fmicb.2023.1143770

Gen Psychiatr. 2023 Mar 13;36(2):e100996. doi: 10.1136/gpsych-2022-100996. eCollection 2023.

ABSTRACT

BACKGROUND: Type 2 diabetes (T2D) is a chronic metabolic disorder with high comorbidity with mental disorders. The genetic links between attention-deficit/hyperactivity disorder (ADHD) and T2D have yet to be elucidated.

AIMS: We aim to assess shared genetics and potential associations between ADHD and T2D.

METHODS: We performed genetic correlation, two-sample Mendelian randomisation and polygenic overlap analyses between ADHD and T2D. The genome-wide association study (GWAS) summary results of T2D (80 154 cases and 853 816 controls), ADHD2019 (20 183 cases and 35 191 controls from the 2019 GWAS ADHD dataset) and ADHD2022 (38 691 cases and 275 986 controls from the 2022 GWAS ADHD dataset) were used for the analyses. The T2D dataset was obtained from the DIAGRAM Consortium. The ADHD datasets were obtained from the Psychiatric Genomics Consortium. We compared genome-wide association signals to reveal shared genetic variation between T2D and ADHD using the larger ADHD2022 dataset. Moreover, molecular pathways were constructed based on large-scale literature data to understand the connection between ADHD and T2D.

RESULTS: T2D has positive genetic correlations with ADHD2019 (rg=0.33) and ADHD2022 (rg=0.31). Genetic liability to ADHD2019 was associated with an increased risk for T2D (odds ratio (OR): 1.30, p<0.001), while genetic liability to ADHD2022 had a suggestive causal effect on T2D (OR: 1.30, p=0.086). Genetic liability to T2D was associated with a higher risk for ADHD2019 (OR: 1.05, p=0.001) and ADHD2022 (OR: 1.03, p<0.001). The polygenic overlap analysis showed that most causal variants of T2D are shared with ADHD2022. T2D and ADHD2022 have three overlapping loci. Molecular pathway analysis suggests that ADHD and T2D could promote the risk of each other through inflammatory pathways.

CONCLUSIONS: Our study demonstrates substantial shared genetics and bidirectional causal associations between ADHD and T2D.

PMID:36937092 | PMC:PMC10016243 | DOI:10.1136/gpsych-2022-100996

Ecol Evol. 2023 Mar 16;13(3):e9914. doi: 10.1002/ece3.9914. eCollection 2023 Mar.

ABSTRACT

How environmental factors shape patterns of biotic diversity in tropical ecosystems is an active field of research, but studies examining the possibility of ecological speciation in terrestrial tropical ecosystems are scarce. We use the isolated rainforest herpetofauna on the Montagne d'Ambre (Amber Mountain) massif in northern Madagascar as a model to explore elevational divergence at the level of populations and communities. Based on intensive sampling and DNA barcoding of amphibians and reptiles along a transect ranging from ca. 470-1470 m above sea level (a.s.l.), we assessed a main peak in species richness at an elevation of ca. 1000 m a.s.l. with 41 species. The proportion of local endemics was highest (about 1/3) at elevations >1100 m a.s.l. Two species of chameleons (Brookesia tuberculata, Calumma linotum) and two species of frogs (Mantidactylus bellyi, M. ambony) studied in depth by newly developed microsatellite markers showed genetic divergence up the slope of the mountain, some quite strong, others very weak, but in each case with genetic breaks between 1100 and 1270 m a.s.l. Genetic clusters were found in transect sections significantly differing in bioclimate and herpetological community composition. A decrease in body size was detected in several species with increasing elevation. The studied rainforest amphibians and reptiles show concordant population genetic differentiation across elevation along with morphological and niche differentiation. Whether this parapatric or microallopatric differentiation will suffice for the completion of speciation is, however, unclear, and available phylogeographic evidence rather suggests that a complex interplay between ecological and allopatric divergence processes is involved in generating the extraordinary species diversity of Madagascar's biota. Our study reveals concordant patterns of diversification among main elevational bands, but suggests that these adaptational processes are only part of the complex of processes leading to species formation, among which geographical isolation is probably also important.

PMID:36937068 | PMC:PMC10019947 | DOI:10.1002/ece3.9914

Front Aging. 2023 Mar 1;4:1057204. doi: 10.3389/fragi.2023.1057204. eCollection 2023.

ABSTRACT

While in the past technology has mostly been utilized to store information about the structural configuration of proteins and molecules for research and medical purposes, Artificial Intelligence is nowadays able to learn from the existing data how to predict and model properties and interactions, revealing important knowledge about complex biological processes, such as aging. Modern technologies, moreover, can rely on a broader set of information, including those derived from the next-generation sequencing (e.g., proteomics, lipidomics, and other omics), to understand the interactions between human body and the external environment. This is especially relevant as external factors have been shown to have a key role in aging. As the field of computational systems biology keeps improving and new biomarkers of aging are being developed, artificial intelligence promises to become a major ally of aging research.

PMID:36936271 | PMC:PMC10018490 | DOI:10.3389/fragi.2023.1057204

Curr Res Food Sci. 2023 Mar 2;6:100471. doi: 10.1016/j.crfs.2023.100471. eCollection 2023.

ABSTRACT

Gluten composition is an important quality parameter for wheat flour, because it is strongly correlated to baking quality. Wheat proteins are commonly extracted stepwise and analysed using RP-HPLC-UV to determine the gluten composition. This procedure is very time-consuming and labour-intensive. Therefore, a new, fast and easy method to quantitate gluten proteins was established using NIR spectroscopy (NIRS). PLS-regression models were calculated containing 207 samples for calibration and 169 for test set validation. Albumin/globulin (ALGL), gluten, gliadin and glutenin content was predicted with a root mean square error of prediction (RMSEP) of 2.01 mg/g, 6.09 mg/g, 4.25 mg/g and 3.50 mg/g, respectively. High-molecular-weight glutenin subunits (HMW-GS) and low-molecular-weight glutenin subunits (LMW-GS) were predicted with a RMSEP of 1.12 mg/g and 2.38 mg/g. The relative error was too high for ALGL, LMW-GS and HMW-GS, but that of gluten, gliadins and glutenins was in a range comparable to the reference method. Therefore, the new NIRS method can be used to estimate the gluten composition of wheat flour, including the gliadin/glutenin and the LMW-GS/HMW-GS ratio.

PMID:36935851 | PMC:PMC10017355 | DOI:10.1016/j.crfs.2023.100471

Mol Ther. 2023 Mar 17:S1525-0016(23)00135-1. doi: 10.1016/j.ymthe.2023.03.014. Online ahead of print.

ABSTRACT

The Human T-cell leukaemia virus type 1 (HTLV-1) is a pathogenic retrovirus that persists as a provirus in the genome of infected cells and can lead to adult T-cell leukaemia (ATL). Worldwide, more than 10 million people are infected and approximately 5% of these individuals will develop ATL, a highly aggressive cancer that is currently incurable. In the last years, genome editing tools have emerged as promising antiviral agents. In this proof-of-concept study, we used substrate linked directed evolution (SLiDE) to engineer Cre-derived site-specific recombinases to excise the HTLV-1 proviral genome from infected cells. We identified a conserved loxP-like sequence (loxHTLV) present in the long terminal repeats of the majority of virus isolates. After 181 cycles of SLiDE, we isolated a designer-recombinase (designated RecHTLV), which efficiently recombines the loxHTLV sequence in bacteria and human cells with high specificity. Expression of RecHTLV in human Jurkat T-cells resulted in antiviral activity when challenged with an HTLV-1 infection. Moreover, expression of RecHTLV in chronically infected SP cells led to the excision of HTLV-1 proviral DNA. Our data suggest that recombinase mediated excision of the HTLV-1 provirus represents a promising approach to reduce proviral load in HTLV-1 infected individuals, potentially preventing the development of HTLV-1 associated diseases.

PMID:36934299 | DOI:10.1016/j.ymthe.2023.03.014

Commun Biol. 2023 Mar 18;6(1):288. doi: 10.1038/s42003-023-04687-4.

ABSTRACT

SARS-CoV-2 has extensively mutated creating variants of concern (VOC) resulting in global infection surges. The Omicron VOC reinfects individuals exposed to earlier variants of SARS-CoV-2 at a higher frequency than previously seen for non-Omicron VOC. An analysis of the sub-lineages associated with an Omicron primary infection and Omicron reinfection reveals that the incidence of Omicron-Omicron reinfections is occurring over a shorter time interval than seen after a primary infection with a non-Omicron VOC. Our analysis suggests that a single infection from SARS-CoV-2 may not generate the protective immunity required to defend against reinfections from emerging Omicron lineages. This analysis was made possible by Next-generation sequencing (NGS) of a Danish cohort with clinical metadata on both infections occurring in the same individual. We suggest that the continuation of COVID-19 NGS and inclusion of clinical metadata is necessary to ensure effective surveillance of SARS-CoV-2 genomics, assist in treatment and vaccine development, and guide public health recommendations.

PMID:36934204 | DOI:10.1038/s42003-023-04687-4

Sci Rep. 2023 Mar 18;13(1):4516. doi: 10.1038/s41598-022-26558-4.

ABSTRACT

Heterogeneity in the Diabetic Kidney Disease (DKD) diagnosis makes its rational therapeutics challenging. Although albuminuria characterizes DKD, reports also indicate its prevalence among non-proteinuric. Recent understanding of disease progression has thus inclined the focus on proximal tubular cell damage besides the glomeruli. A non-invasive approach exploiting exosomal miRNA derived from human kidney proximal tubular cell line was, hence, targeted. Upon miRNA profiling, three miRNAs, namely, hsa-miR-155-5p, hsa-miR-28-3p, and hsa-miR-425-5p were found to be significantly upregulated, while hsa-miR-663a was downregulated under diabetic conditions. Among these, hsa-miR-663a downregulation was more pronounced in non-proteinuric than proteinuric DKD subjects and was thus selected for the bioinformatics study. Ingenuity Pathway Analysis (IPA) narrowed on to IL-8 signaling and inflammatory response as the most enriched 'canonical pathway' and 'disease pathway' respectively, during DKD. Further, the putative gene network generated from these enriched pathways revealed experimentally induced diabetes, renal tubular injury, and decreased levels of albumin as part of mapping under 'disease and function'. Genes target predictions and annotations by IPA reiterated miR-663a's role in the pathogenesis of DKD following tubular injury. Overall, the observations might offer an indirect reflection of the underlying mechanism between patients who develop proteinuria and non-proteinuria.

PMID:36934129 | DOI:10.1038/s41598-022-26558-4

J Mol Biol. 2023 Mar 16:168052. doi: 10.1016/j.jmb.2023.168052. Online ahead of print.

ABSTRACT

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural clues within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) clue is derived from template-based modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ∼1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of-the-art resource that offers an unprecedented structure-informed view of the human interactome.

PMID:36933822 | DOI:10.1016/j.jmb.2023.168052

J Thorac Cardiovasc Surg. 2023 Mar 16:S0022-5223(23)00247-7. doi: 10.1016/j.jtcvs.2023.03.009. Online ahead of print.

ABSTRACT

OBJECTIVES: Pulmonary emphysema is characterized by the destruction of alveolar units and reduced gas exchange capacity. In the present study, we aimed to deliver induced pluripotent stem cell (iPSCs)-derived endothelial cells and pneumocytes to repair and regenerate distal lung tissue in an elastase-induced emphysema model.

METHODS: We induced emphysema in athymic rats via intratracheal injection of elastase as previously reported. At 21 and 35 days after elastase treatment, we suspended 80 million iPSCs-derived endothelial cells and 20 million iPSCs-derived pneumocytes in hydrogel and injected the mixture intra-tracheally. On day 49 after elastase treatment, we performed imaging, functional analysis, and collected lungs for histology.

RESULTS: Using immunofluorescence detection of human-specific HLA1, human-specific CD31, and anti-GFP for the reporter labeled pneumocytes, we found that transplanted cells engrafted in 14.69 ± 0.95% of the host alveoli and fully integrated to form vascularized alveoli together with host cells. Transmission electron microscopy confirmed the incorporation of the transplanted human cells and the formation of a blood-air barrier. Human endothelial cells formed perfused vasculature. Computed tomography scans revealed improved vascular density and decelerated emphysema progression in cell-treated lungs. Proliferation of both human and rat cell was higher in cell-treated vs non-treated controls. Cell treatment reduced alveolar enlargement, improved dynamic compliance and residual volume, and improved diffusion capacity.

CONCLUSIONS: Our findings suggest that human iPSC-derived distal lung cells can engraft in emphysematous lungs and participate in the formation of functional distal lung units to ameliorate the progression of emphysema.

PMID:36933786 | DOI:10.1016/j.jtcvs.2023.03.009

J Biomed Inform. 2023 Mar 16:104341. doi: 10.1016/j.jbi.2023.104341. Online ahead of print.

ABSTRACT

BACKGROUND: Pharmacokinetic natural product-drug interactions (NPDIs) occur when botanical or other natural products are co-consumed with pharmaceutical drugs. With the growing use of natural products, the risk for potential NPDIs and consequent adverse events has increased. Understanding mechanisms of NPDIs is key to preventing or minimizing adverse events. Although biomedical knowledge graphs (KGs) have been widely used for drug-drug interaction applications, computational investigation of NPDIs is novel. We constructed NP-KG as a first step toward computational discovery of plausible mechanistic explanations for pharmacokinetic NPDIs that can be used to guide scientific research.

METHODS: We developed a large-scale, heterogeneous KG with biomedical ontologies, linked data, and full texts of the scientific literature. To construct the KG, biomedical ontologies and drug databases were integrated with the Phenotype Knowledge Translator framework. The semantic relation extraction systems, SemRep and Integrated Network and Dynamic Reasoning Assembler, were used to extract semantic predications (subject-relation-object triples) from full texts of the scientific literature related to the exemplar natural products green tea and kratom. A literature-based graph constructed from the predications was integrated into the ontology-grounded KG to create NP-KG. NP-KG was evaluated with case studies of pharmacokinetic green tea- and kratom-drug interactions through KG path searches and meta-path discovery to determine congruent and contradictory information in NP-KG compared to ground truth data. We also conducted an error analysis to identify knowledge gaps and incorrect predications in the KG.

RESULTS: The fully integrated NP-KG consisted of 745,512 nodes and 7,249,576 edges. Evaluation of NP-KG resulted in congruent (38.98% for green tea, 50% for kratom), contradictory (15.25% for green tea, 21.43% for kratom), and both congruent and contradictory (15.25% for green tea, 21.43% for kratom) information compared to ground truth data. Potential pharmacokinetic mechanisms for several purported NPDIs, including the green tea-raloxifene, green tea-nadolol, kratom-midazolam, kratom-quetiapine, and kratom-venlafaxine interactions were congruent with the published literature.

CONCLUSION: NP-KG is the first KG to integrate biomedical ontologies with full texts of the scientific literature focused on natural products. We demonstrate the application of NP-KG to identify known pharmacokinetic interactions between natural products and pharmaceutical drugs mediated by drug metabolizing enzymes and transporters. Future work will incorporate context, contradiction analysis, and embedding-based methods to enrich NP-KG. NP-KG is publicly available at Doi:10.5281/zenodo.6814507. The code for relation extraction, KG construction, and hypothesis generation is available at https://github.com/sanyabt/np-kg.

PMID:36933632 | DOI:10.1016/j.jbi.2023.104341

Int J Biol Macromol. 2023 Mar 16:124061. doi: 10.1016/j.ijbiomac.2023.124061. Online ahead of print.

ABSTRACT

In plants, sexual reproduction relies on the proper development of floral organs that facilitate the successful development of fruits and seeds. Auxin responsive small auxin-up RNA (SAUR) genes play essential roles in floral organ formation and fruit development. However, little is known about the role of SAUR genes in pineapple floral organ formation and fruit development as well as stress responses. In this study, based on genome information and transcriptome datasets, 52 AcoSAUR genes were identified and grouped into 12 groups. The gene structure analysis revealed that most AcoSAUR genes did not have introns, although auxin-acting elements were abundant in the promoter region of AcoSAUR members. The expression analysis across the multiple flower and fruit development stages revealed differential expression of AcoSAUR genes, indicating a tissue and stage-specific function of AcoSAURs. Correlation analysis and pairwise comparisons between gene expression and tissue specificity identified stamen-, petal-, ovule-, and fruit-specific AcoSAURs involved in pineapple floral organs (AcoSAUR4/5/15/17/19) and fruit development (AcoSAUR6/11/36/50). RT-qPCR analysis revealed that AcoSAUR12/24/50 played positive roles in response to the salinity and drought treatment. This work provides an abundant genomic resource for functional analysis of AcoSAUR genes during the pineapple floral organs and fruit development stages. It also highlights the role of auxin signaling involved in pineapple reproductive organ growth.

PMID:36933586 | DOI:10.1016/j.ijbiomac.2023.124061

Horm Behav. 2023 Mar 16;151:105340. doi: 10.1016/j.yhbeh.2023.105340. Online ahead of print.

ABSTRACT

Organismal behavior, with its tremendous complexity and diversity, is generated by numerous physiological systems acting in coordination. Understanding how these systems evolve to support differences in behavior within and among species is a longstanding goal in biology that has captured the imagination of researchers who work on a multitude of taxa, including humans. Of particular importance are the physiological determinants of behavioral evolution, which are sometimes overlooked because we lack a robust conceptual framework to study mechanisms underlying adaptation and diversification of behavior. Here, we discuss a framework for such an analysis that applies a "systems view" to our understanding of behavioral control. This approach involves linking separate models that consider behavior and physiology as their own networks into a singular vertically integrated behavioral control system. In doing so, hormones commonly stand out as the links, or edges, among nodes within this system. To ground our discussion, we focus on studies of manakins (Pipridae), a family of Neotropical birds. These species have numerous physiological and endocrine specializations that support their elaborate reproductive displays. As a result, manakins provide a useful example to help imagine and visualize the way systems concepts can inform our appreciation of behavioral evolution. In particular, manakins help clarify how connectedness among physiological systems-which is maintained through endocrine signaling-potentiate and/or constrain the evolution of complex behavior to yield behavioral differences across taxa. Ultimately, we hope this review will continue to stimulate thought, discussion, and the emergence of research focused on integrated phenotypes in behavioral ecology and endocrinology.

PMID:36933440 | DOI:10.1016/j.yhbeh.2023.105340

Adv Mater. 2023 Mar 18:e2212065. doi: 10.1002/adma.202212065. Online ahead of print.

ABSTRACT

Many bacteria use protein-based organelles known as bacterial microcompartments (BMCs) to organize and sequester sequential enzymatic reactions. Regardless of their specialized metabolic function, all BMCs are delimited by a shell made of multiple structurally redundant, yet functionally diverse, hexameric (BMC-H), pseudohexameric/trimeric (BMC-T), or pentameric (BMC-P) shell protein paralogs. When expressed without their native cargo, shell proteins have been shown to self-assemble into 2D sheets, open-ended nanotubes and closed shells of ∼40 nm diameter that are being developed as scaffolds and nanocontainers for applications in biotechnology. Here by leveraging a strategy for affinity-based purification we demonstrate that a wide range of empty synthetic shells, many differing in end cap structures, can be derived from a glycyl radical enzyme-associated microcompartment (GRM). The range of pleomorphic shells observed, which span ∼2 orders of magnitude in size from ∼25 nm to ∼1.8 μm, reveals the remarkable plasticity of BMC-based biomaterials. In addition, we observe new capped nanotube and nanocone morphologies that are consistent with a multi-component geometric model in which architectural principles are shared among asymmetric carbon, viral protein, and BMC-based structures. This article is protected by copyright. All rights reserved.

PMID:36932732 | DOI:10.1002/adma.202212065

Plant Physiol. 2023 Mar 18:kiad166. doi: 10.1093/plphys/kiad166. Online ahead of print.

NO ABSTRACT

PMID:36932717 | DOI:10.1093/plphys/kiad166

Phys Rev E. 2023 Feb;107(2-1):024116. doi: 10.1103/PhysRevE.107.024116.

ABSTRACT

Many dynamical systems, from quantum many-body systems to evolving populations to financial markets, are described by stochastic processes. Parameters characterizing such processes can often be inferred using information integrated over stochastic paths. However, estimating time-integrated quantities from real data with limited time resolution is challenging. Here, we propose a framework for accurately estimating time-integrated quantities using Bézier interpolation. We applied our approach to two dynamical inference problems: Determining fitness parameters for evolving populations and inferring forces driving Ornstein-Uhlenbeck processes. We found that Bézier interpolation reduces the estimation bias for both dynamical inference problems. This improvement was especially noticeable for data sets with limited time resolution. Our method could be broadly applied to improve accuracy for other dynamical inference problems using finitely sampled data.

PMID:36932614 | DOI:10.1103/PhysRevE.107.024116