The Ensemble of Gene Regulatory Networks at Mutation-Selection Balance The evolution of diverse phenotypes both involves and is constrained by molecular interaction networks. When these networks influence patterns of expression, we refer to them as gene regulatory networks (GRNs). Here, we develop a quasi-species model of GRN evolution. With this model, we prove that-across a broad spectrum of viability and mutation functions-the dynamics converge to a stationary distribution over GRNs. Next, we show from first principles how the frequency of GRNs at equilibrium will be proportional to each GRNs eigenvector centrality in the genotype network. Finally, we determine the structural characteristics of GRNs that are favored in response to a range of selective environments and mutational constraints. Our work connects GRN evolution to quasi-species models, and thus can provide a mechanistic explanation for the topology of GRNs experiencing various evolutionary forces.
Mutation rate dynamics reflect ecological change in an emerging zoonotic pathogen While mutation is often deleterious, it can also be adaptive. Mutation rates vary both within and between bacterial species, and understanding what drives this variation is essential for understanding the evolutionary dynamics of bacterial populations. In this study we investigate two factors that are predicted to influence the trade-off between the costs and benefits of mutation: ecology and genome size. To investigate the relationship between these factors and mutation rate we conducted mutation accumulation experiments on eight strains of the emerging zoonotic pathogen Streptococcus suis. Natural variation within this species allows us to compare tonsil carriage and invasive disease isolates, from both more and less pathogenic populations, with a wide range of genome sizes. We find that invasive disease isolates have repeatedly evolved mutation rates that are higher than those of closely related carriage isolates, regardless of variation in genome size. Independent of this variation in overall rate, we also observe a stronger bias towards G/C to A/T mutations in isolates from more pathogenic populations, whose genomes tend to be smaller and more AT-rich. Our results suggest that ecology is a stronger correlate of mutation rate than genome size over these timescales, and that transitions to invasive disease are consistently accompanied by rapid increases in mutation rate. These results shed light on the impact ecology can have on the adaptive potential of bacterial pathogens.
Re-convolving the compositional landscape of primary and recurrent glioblastoma reveals prognostic andtargetable tissue states Glioblastoma is an aggressive diffusely infiltrating neoplasm that spreads beyond surgical resection margins, where it intermingles with non-neoplastic brain cells. This complex microenvironment harbouring infiltrating glioma and non-neoplastic brain cells is the origin of tumor recurrence. Thus, understanding the cellular and molecular features of the glioma microenvironment is therapeutically and prognostically important. Here, we used single-nucleus RNA sequencing (snRNAseq) of primary and recurrent glioma to define three compositional  tissue-states rooted in cohabitation of cell-types and transcriptional states. These comprise a state featuring A) abundant normal brain microenvironment cells, B) reactive/inflamed brain microenvironment, and C) cellular/proliferative tumor. All these states exhibited variable degrees of infiltration by glioma cells. Spatial transcriptomics confirmed that the cell-types and transcriptomics states which compositionally cohabitate tissue states also colocalize in space. Tissue states are clinically significant because they correlate with radiographic, histopathologic, and prognostic features. We found that enrichment of tissue state B signature correlated with shorter survival. Importantly, we found that our compositionally defined tissue states are enriched in distinct metabolic pathways. One such pathway is fatty acid biosynthesis, which was enriched in tissue state B - a state enriched in recurrent glioblastoma and composed of astrocyte-like/mesenchymal glioma cells, reactive astrocytes resembling those seen in neurodegeneration, and monocyte-like myeloid cells. We showed that treating acute slices of GBM with a fatty acid synthesis inhibitor is sufficient to deplete the transcriptional signature of tissue state B. Our findings define a novel compositional approach to the glioma-infiltrated tissue which allows us to discover prognostic and targetable features, paving the way to new mechanistic and therapeutic discoveries. Meiotic DSB-independent role of protein phosphatase 4 in Hop1 assembly to promote meiotic chromosome axis formation in budding yeast Dynamic changes in chromosomal structure that occur during meiotic prophase play an important role in the progression of meiosis. Among these structures, meiosis-specific chromosomal axis-loop structures are important as a scaffold for integrated control between the meiotic recombination reaction and the associated checkpoint system to ensure accurate chromosome segregation. However, the molecular mechanism of the initial step of chromosome axis-loop construction is not well understood. Here, we showed that in budding yeast, protein phosphatase 4 (PP4) that primarily counteracts Mec1/Tel1 phosphorylation is required to promote the assembly of chromosomal axis components Hop1 and Red1 onto meiotic chromatin via interaction with Hop1. PP4, in contrast, did not affect Rec8 assembly. Notably, unlike the previously known function of PP4, this novel function of PP4 was independent of meiotic DSB-dependent Tel1/Mec1 kinase activities. The defect in Hop1/Red1 assembly in the absence of PP4 function was not suppressed by dysfunction of Pch2, which removes Hop1 protein from the chromosome axis. This suggests that PP4 is required for the initial step of chromatin loading of Hop1 rather than stabilization of Hop1 on axes. These results indicate that phosphorylation/dephosphorylation-mediated regulation of Hop1 recruitment onto chromatin occurs during chromosome axis construction before meiotic double-strand break formation. Alpha-Frag: a deep neural network for fragment presence prediction improves peptide identification Identification of peptides in mass spectrometry-based proteomics typically relies on spectra matches. As MS/MS spectra record presence and intensity of fragment ions, the match should take both fragment presence similarity and intensity similarity into consideration. Fragment presence similarity can be calculated with the help of fragment presence prediction such as theoretical enumeration of all possible fragment ions or selecting non-zero intensity ions from the result of fragment intensity prediction, but neither of these two methods is accurate enough. In this work, we developed a deep neural network based model, Alpha-Frag, to predict precisely the fragment ions that should be present for a given peptide. Alpha-Frag modelled fragment presence prediction as a multi-label classification task and trained with ProteomeTools dataset. In terms of intersection over union (IoU), Alpha-Frag achieved an average of >0.7 and outperformed the benchmarks across the validation datasets. Furthermore, fragment presence similarity was calculated based on presence prediction and incorporated into the peptide statistical validation tools as an additional score to improve peptide identifications. Our preliminary experiments show that this score led to a maximum increase of 26.8% (FDR 0.1%) and 21.6% (FDR 1%) for the DDA and the DIA identification, respectively.

Significance StatementA better prediction of fragmentation for peptides in mass spectrometry (MS) is beneficial to the peptide identification. As the MS/MS spectra record two-dimensional information of fragment ions derived from precursors, mass-to-charge ratio (m/z) and their corresponding intensities, besides the fragment intensity prediction, it is necessary to study the presence prediction. Although the presence prediction can be realized by enumerating all the possible fragmentation patterns of a peptide with equal probability or by selecting non-zero intensity fragment ions from the result of fragment intensity prediction, neither of these two methods is accurate enough. In this study, deep learning is leveraged to precisely predict the fragment ions of a given peptide. Based on the fragment presence prediction, fragment presence similarity between experimental spectra and predicted spectra can be calculated which is proved to promote the peptide detections both for DDA and for DIA data. Severe COVID-19 associated variants linked to chemokine receptor gene control in monocytes and macrophages Genome-wide association studies have identified 3p21.31 as the main risk locus for severe disease in COVID-19 patients, although underlying biological mechanisms remain elusive. We performed a comprehensive epigenomic dissection of the 3p21.31 locus, identifying a CTCF-dependent tissue-specific 3D regulatory chromatin hub that controls the activity of several tissue-homing chemokine receptor (CCR) genes in monocytes and macrophages. Risk SNPs colocalized with regulatory elements and were linked to increased expression of CCR1, CCR2 and CCR5 in monocytes and macrophages. As excessive organ infiltration of inflammatory monocytes and macrophages is a hallmark of severe COVID-19, our findings provide a rationale for the genetic association of 3p21.31 variants with elevated risk of hospitalization upon SARS-CoV-2 infection. A computational modeling approach for predicting multicell patterns based on signaling-induced differential adhesion Differential adhesion within cell populations enables the emergence of unique patterns in heterogeneous multicellular systems. This process has been previously explored using synthetically engineered heterogenous multicell spheroid systems, in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models may be leveraged to more systematically explore the key parameters that drive the emergence of different patterns. We developed and validated two- and three-dimensional agent-based models (ABMs) of spheroid patterning for cells engineered with a bidirectional signaling circuit that regulates N- and P-cadherin expression. The model was used to predict how varying initial cell seedings, cadherin induction probabilities, or homotypic adhesion strengths between cells impact spheroid patterning, and unsupervised machine learning techniques were used to map system parameters to unique spheroid patterns. The model was then deployed to design new synthetic cell signaling circuits based on a desired final multicell pattern. Assessment of Sex Bias in Housekeeping Gene Expression in Adipose Tissue Through the Massive Analysis of Transcriptomics Data As the housekeeping genes (HKGs) generally involved in maintaining essential cell functions are typically assumed to exhibit constant expression levels across cell types, they are commonly employed as internal controls in gene expression studies. Nevertheless, multiple studies indicate that not all HKGs display stable expression across cells and tissues and under various healthy and diseased conditions, which can introduce systematic errors into experimental results. The selection and validation of HKGs as controls for each studied condition represent crucial steps in ensuring the validity of obtained results; however, up till now, sex has not been typically considered as a biological variable.

In this study, we evaluate the expression profiles of six classical HKGs (four metabolic: GAPDH, HPRT, PPIA, and UBC, and two ribosomal: 18S and RPL19) to determine expression stability in adipose tissues of Homo sapiens and Mus musculus and check sex bias and their overall suitability as internal controls. We also assess the stabile expression of genes included in distinct whole-transcriptome microarrays available from the Gene Expression Omnibus database (GEO) to identify sex-unbiased HKGs suitable for use as internal controls. We perform a sex-based analysis to describe any sexual dimorphisms in mRNA expression stability.

A novel computational strategy based on meta-analysis techniques proves that certain classical HKGs fail to function adequately as controls when analyzing human adipose tissue (HAT) considering sex as a variable. The extensively used 18S gene displays sex-based variability in adipose tissue, although PPIA and RPL19 do not, and hence, represent robust HKGs. We propose new sex-unbiased human and mouse HKGs (suHKG) derived from sex-specific expression profiles, such as RPS8 and UBB. All results generated during this study are readily available by accessing an open web resource (https://bioinfo.cipf.es/metafun-HKG) for consultation and reuse in further studies. Integrated annotation and analysis of genomic features reveal new types of functional elements and large-scale epigenetic phenomena in the developing zebrafish Zebrafish, a popular model for embryonic development and for modelling human diseases, has so far lacked a systematic functional annotation programme akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created the first central repository to store and process zebrafish developmental functional genomic data. Our Data Coordination Center (https://danio-code.zfin.org) combines a total of 1,802 sets of unpublished and reanalysed published genomics data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements in development, including novel classes with distinct features dependent on their activity in time and space. We delineated the distinction between regulatory elements active during zygotic genome activation and those active during organogenesis, identifying new aspects of how they relate to each other. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predict functional relationships between them beyond sequence similarity, extending the utility of zebrafish developmental genomics to mammals. The 55K SNP-Based Exploration of QTL for Spikelet Number Per Spike in a Tetraploid Wheat (Triticum turgidum L.) Population: Chinese Landrace Ailanmai X Wild Emmer The spikelet number per spike (SNS) is a primary factor determining wheat yield. Common wheat breeding reduces the genetic diversity among elite germplasm resources, leading to a detrimental effect on future wheat production. It is, therefore, necessary to explore new genetic resources for SNS to increase wheat yield. A tetraploid landrace  Ailanmai x wild emmer wheat recombinant inbred line (RIL) population was used to construct a genetic map using the wheat 55K single nucleotide polymorphism (SNP) array. The linkage map containing 1150 bin markers with a total genetic distance of 2411.8 cM was obtained. Based on phenotypic data from eight environments and best linear unbiased prediction (BLUP) values, five quantitative trait loci (QTL) for SNS were identified, explaining 6.71-29.40% of the phenotypic variation. Two of them, QSns.sau-AM-2B.3 and QSns.sau-AM-3B.2, were detected as the major and novel QTL. Their effects were further validated in two additional F2 populations using tightly linked Kompetitive Allele-Specific PCR (KASP) markers. Potential candidate genes within the physical intervals of corresponding QTL were predicted to participate in inflorescence development and spikelet formation. Genetic associations between SNS and other agronomic traits were also detected and analyzed. This study demonstrates the feasibility of wheat 55K SNP array developed for common wheat in genetic mapping of tetraploid population and shows the potential application of wheat related species in wheat improvement programs. INPP4B drives lysosome biogenesis to restrict leukemic stem cell differentiation and promote leukemogenesis Signaling pathways that control vital features of leukemic stem cells including multipotency, self-renewal, clonal expansion and quiescence remain unclear. Emerging studies illustrate critical roles for lysosomes in hematopoietic and leukemic stem cell fate. By investigating consequences of INPP4B alterations in AML, we have discovered its role in driving leukemic  stemness. We observed that INPP4B is highly expressed leukemic stem cell populations and Inpp4b-deficeint leukemias demonstrate increased disease latency, reduced leukemia initiating potential which is associated with a differentiated leukemic phenotype. Molecular analyses show that Inpp4b-deficient leukemias have compromised lysosomal gene expression, lysosomal content, and lysosomal activity. Our discovery of a novel pathway linking INPP4B, lysosomal biogenesis and leukemic stemness, provides a mechanism to explain the association of high INPP4B expression with poor AML prognosis, and highlights novel patient stratification strategies and LSC-specific leukemic therapies.

Key PointsOur findings highlight a novel pathway linking INPP4B, lysosomal function and leukemic stemness that explains the prognostic role of INPP4B in AML.
Our data reveal the utility of INPP4B as a biomarker of aggressive AML and provide a rationale to explore INPP4B and its associated function in lysosome biology as novel strategies to target LSC and AML Positive and negative effects of land abandonment on butterfly communities revealed by a hierarchical sampling design across climatic regions Land abandonment may decrease biodiversity but also provides an opportunity for rewilding. It is therefore necessary to identify areas that may benefit from traditional land management practices and those that may benefit from a lack of human intervention. In this study, we conducted comparative field surveys of butterfly occurrence in abandoned and inhabited settlements in 18 regions of diverse climatic zones in Japan to test the hypotheses that species-specific responses to land abandonment correlate with climatic niches and habitat preferences. Hierarchical models that unified species occurrence and habitat preferences revealed that negative responses to land abandonment were associated with species that have cold climatic niches and utilize open habitats, suggesting that species negatively impacted by land abandonment will decline more due to future climate warming. Maps representing species gains and losses due to land abandonment, which were created from the model estimates, showed similar geographic patterns, but some areas exhibited high species losses relative to gains. Our hierarchical modelling approach was useful for scaling up local-scale effects of land abandonment to a macro-scale assessment, which is crucial to developing spatial conservation strategies in the era of depopulation. The DEAD box RNA helicase DDX42 is an intrinsic inhibitor of positive-strand RNA viruses Genome-wide screens are powerful approaches to unravel new regulators of viral infections. Here, we used a CRISPR/Cas9 screen to reveal new HIV-1 inhibitors. This approach led us to identify the RNA helicase DDX42 as an intrinsic antiviral inhibitor. DDX42 was previously described as a non-processive helicase, able to bind RNA secondary structures such as G-quadruplexes, with no clearly defined function ascribed. Our data show that depletion of endogenous DDX42 significantly increased HIV-1 DNA accumulation and infection in cell lines and primary cells. DDX42 overexpression inhibited HIV-1, whereas a dominant-negative mutant increased infection. Importantly, DDX42 also restricted retrotransposition of LINE-1, infection with other retroviruses and positive-strand RNA viruses, including CHIKV and SARS-CoV-2. However, DDX42 did not inhibit infection with three negative-strand RNA viruses, arguing against a general, unspecific effect on target cells, which was confirmed by RNA-seq analysis. DDX42 was found in the vicinity of viral elements by proximity ligation assays, and cross-linking RNA immunoprecipitation confirmed a specific interaction of DDX42 with RNAs from sensitive viruses. This strongly suggested a direct mode of action of DDX42 on viral ribonucleoprotein complexes. Taken together, our results show for the first time a new and important role of DDX42 in intrinsic antiviral immunity. Biophysical characterization of the oligomeric states of recombinant Immunoglobulins type-M and their C1q binding kinetics by Biolayer Interferometry The Immunoglobulins type-M (IgMs) are one of the first antibody classes mobilized during immune responses against pathogens and tumor cells. Binding to specific target antigens enables the interaction with the C1 complex which strongly activates the classical complement pathway. This biological function is the basis for the huge therapeutic potential of IgMs but due to their high oligomeric complexity, in vitro production, biochemical and biophysical characterizations are challenging. In the present study, we present recombinant production of two IgM models (IgM617 and IgM012) in pentameric and hexameric states and the evaluation of their polymer distribution using different biophysical methods (AUC, SEC-MALLS, Mass Photometry and Transmission Electron Microscopy). Each IgM oligomer has individual specific expression pattern and yield with different protein quality likely due to intrinsic IgM properties and patterning. Nevertheless, the purified recombinant IgMs retain their ability to activate complement in a C1q dependent manner. And more importantly, a new method to evaluate their functional quality attribute by characterizing the kinetics of C1q binding to recombinant IgM has been developed using BioLayer Interferometry (BLI). We show that recombinant IgMs possess similar C1q binding properties as IgMs purified from human plasma. A Hierarchy of Biomolecular Proportional-Integral-Derivative Feedback Controllers for Robust Perfect Adaptation and Dynamic Performance Proportional-Integral-Derivative (PID) feedback controllers have been the most widely used controllers in industry for almost a century due to their good performance, simplicity, and ease of tuning. Motivated by their success in various engineering disciplines, PID controllers recently found their way into synthetic biology, where the design of feedback molecular control systems has been identified as an important goal. In this paper, we consider the mathematical realization of PID controllers via biomolecular interactions. We propose an array of topologies that offer a compromise between simplicity and high performance. We first demonstrate that different Proportional-Integral (PI) controllers exhibit different capabilities for enhancing the dynamics and reducing variance (cell-to-cell variability). Next, we introduce several derivative controllers that are realized based on incoherent feedforward loops acting in a feedback configuration. Alternatively, we show that differentiators can be realized by placing molecular integrators in a negative feedback loop--an arrangement that can then be augmented by PI components to yield PID feedback controllers. We demonstrate that the derivative component can be exploited for enhancing system stability, dramatically increasing the molecular control systems dynamic performance, and for reducing the noise effect on the output of interest. The PID controller features are established through various deterministic and stochastic analyses as well as numerical simulations. Finally, we provide an experimental demonstration using a recently developed hybrid setup, the cyberloop, where the controller is implemented in silico to control a biological genetic circuit in vivo. The large array of novel biomolecular PID controllers introduced here forms a basis for the design and construction of advanced high-performance biomolecular control systems that robustly regulate the dynamics of living systems. Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: the Multi-Targeting Drug DREAM Challenge A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ( polypharmacology). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Author SummaryMany modern drugs are developed with the goal of modulating a single cellular pathway or target. However, many drugs are, in fact,  dirty; they target multiple cellular pathways or targets. This phenomenon is known as multi-targeting or polypharmacology. While some strive to develop  cleaner therapeutics that eliminate secondary targets, recent work has shown that multi-targeting therapeutics have key advantages for a variety of diseases. However, while multi-targeting drugs that affect a precisely-defined profile of targets may be more effective, it is difficult to computationally predict which molecules have desirable target profiles. Here, we report the results of a competitive crowdsourcing project (the Multi-Targeting Drug DREAM Challenge), where we challenged participants to predict chemicals that have desired target profiles for cancer and neurodegenerative disease. Classification, Using Field Potential Images, of Pacemaker Micro-coordination in the Mouse Intestine The flexible and sophisticated movement of the gastrointestinal (GI) tract implies the involvement of mechanisms other than enteric neural circuits, to coordinate excitation in microregions. We thus performed microimaging of pacemaker dynamics in the small intestine of mice since it contains typical network-forming pacemaker cells. A dialysis membrane-reinforced low-impedance microelectrode array (MEA) enabled field potentials over a wide frequency range to be stably measured in microregions. The pacemaker dynamics were classified into basic patterns despite large variations. In the developmental process, pacemaker activity was categorized as either an  expanding or a  migrating pattern that was initiated in or propagated to the MEA sensing area, respectively. The intercellular current of the volume conductor complicated the waveform of both activities. The existence of  expanding and  migrating patterns was attributable to duplicated pacemaker systems such as intracellular Ca2+ oscillation-activated and voltage-gated mechanisms. Additionally, from the spatio-temporal feature during the period of pacemaker events, the  bumpy/aberrant pattern was defined by aberrant, incoherent propagation, and associated with local impairment of excitability, while the  colliding/converging pattern involved the interaction of multiple activities in the MEA area. Interconversion between the four micro-coordination patterns occurred in the same microregion. 5-Hydroxytryptamine (5-HT) promoted  migrating activity, implying an improvement or restoration of spatial conductivity. These results agree well with the action of 5-HT to change GI movement toward propulsion. In conclusion, our MEA method of microimaging classification enables the quantitative assessment of spatio-temporal electric coordination underlying GI motility, suggesting its application to small model animals. Direct Observation of Heterogeneous Formation of Amyloid Spherulites in Real-time by Super-resolution Microscopy The misfolding of proteins and their aggregation in the form of fibrils or amyloid-like spherulites are involved in a spectrum of pathological abnormalities. Our current understanding of protein amyloid aggregation mechanisms has primarily relied on the use of spectrometric methods to determine the average growth rates and diffraction limited microscopes with low temporal resolution to observe the large-scale morphologies of intermediates. We developed a REal-time kinetics via binding and Photobleaching LOcalisation Microscopy (REPLOM) super-resolution method to directly observe and quantify the existence and abundance of diverse aggregate morphologies below the diffraction limit and extract their heterogeneous growth kinetics. Our results revealed that even the growth of a microscopically identical aggregates, e.g. amyloid spherulites, may follow distinct pathways. Specifically, spherulites do not exclusively grow isotropically but, surprisingly, may also grow anisotropically, following similar pathways as reported for minerals and polymers. Combining our technique with machine learning approaches, we associated growth rates to specific morphological transitions and provided energy barriers and the energy landscape at the level of single aggregate morphology. Our unifying framework for the detection and analysis of spherulite growth can be extended to other self-assembled systems characterized by a high degree of heterogeneity, disentangling the broad spectrum of diverse morphologies at the single-molecule level. The coiled-coil domain of E. coli FtsLB is a structurally detuned element critical for modulating its activation in bacterial cell division The FtsLB complex is a key regulator of bacterial cell division, existing in either an off or on state which supports the activation of septal peptidoglycan synthesis. In Escherichia coli, residues known to be critical for this activation are located in a region near the C-terminal end of the periplasmic coiled-coil domain of FtsLB, raising questions about the precise role of this conserved domain in the activation mechanism. Here, we investigate an unusual cluster of polar amino acids found within the core of the FtsLB coiled coil. We hypothesized that these amino acids likely reduce the structural stability of the domain and thus may be important for governing conformational changes. We found that mutating these positions to hydrophobic residues increased the thermal stability of FtsLB but caused cell division defects, suggesting that the coiled-coil domain is a "detuned" structural element. In addition, we identified suppressor mutations within the polar cluster, indicating that the precise identity of the polar amino acids is important for fine-tuning the structural balance between the off and on states. We propose a revised structural model of the tetrameric FtsLB (named the "Y-model") in which the periplasmic domain splits into a pair of coiled-coil branches. In this configuration, the hydrophilic terminal moieties of the polar amino acids remain more favorably exposed to water than in the original four-helix bundle model ("I-model"). We propose that a shift in this architecture, dependent on its marginal stability, is involved in activating the FtsLB complex and triggering septal cell wall reconstruction. RASopathy Mutations Demonstrate the Critical Function of the Cysteine-rich Domain in Maintaining BRAF in an Autoinhibited State to Prevent Aberrant Signaling The RAF kinases are essential effectors of the RAS-GTPases, and germline mutations in BRAF or CRAF are disease drivers in the RASopathy developmental syndromes, with mutations in the cysteine-rich domain (CRD) often observed in BRAF but not CRAF. Although the CRD is known for roles in phosphatidylserine (PS) binding, the RAS-RAF interaction, and RAF autoinhibition, the interplay of these activities and their impact on BRAF function in normal and disease states is unclear. Here, we analyze a panel of BRAF-CRD RASopathy mutations and show that they all increase the biological activity of BRAF by relieving autoinhibition and/or enhancing PS/membrane binding. Strikingly, we find that relief of autoinhibition is the predominant factor determining the severity of the mutations. Comparison of the BRAF and CRAF CRDs further indicates that the BRAF-CRD is a stronger mediator of both autoinhibition and PS binding, and we identify residues contributing to these differences. Moreover, given the increased catalytic activity of BRAF versus CRAF, our studies reveal a more critical role for the CRD and autoinhibition in BRAF regulation and demonstrate that CRD-mediated autoinhibition is required to prevent aberrant BRAF signaling, a finding consistent with the high frequency of mutations that disrupt this function in the RASopathies. Comparison of hybrid learning and remote education in the implementation of the 'Adopt a Microorganism' methodology The Internet has changed the way teachers and students access information and build knowledge. The recent COVID-19 pandemic has created challenges for both teachers and students and a demand for new methodologies of remote learning. In the life sciences, mixing online content with practical activities represents an even greater challenge. In microbiology, the implementation of an active teaching methodology, the #Adopt project, based on the social network Facebook(R), represents an excellent option for connecting remote education with classroom activities. In 2020, the version applied in high school, "Adopt a Microorganism", was adapted to meet the demands of emergency remote education owing to the suppression of face-to-face activities caused by the pandemic. In the present study, we assessed how the change in methodology impacted the discourse richness of students from high school integrated with technical education in the Business Administration program of the Federal Institute of Sao Paulo, Sorocaba Campus. Three questionnaires related to the groups of microorganisms (Archaea, Bacteria, Virus, Fungi, and Protozoan) were applied. The students responses in the 2019 and 2020 classes were compared concerning content richness and multiplicity of concepts through the application of the Shannon diversity index, an approach that is generally used to assess biodiversity in different environments. The observed results suggest that remote learning provided students with a conceptual basis and richness of content equivalent to that achieved by students subjected to the hybrid teaching model. In conclusion, this study suggests that the #Adopt project methodology increases students discourse richness in microbiology even without face-to-face traditional classes. Brain-heart dynamics in emotional regulation under uncertainty indexes depressive risk Recent studies highlight interoception as essential to processing healthy emotion and to pathology of major depressive disorder (MDD). It has been reported that interoceptive responses are impaired in MDD and in healthy individuals with a high depressive risk (HDR). However, it is unclear how individual differences in HDR relate to neurophysiological underpinnings for interoceptive and emotional reactions under different degrees of certainty. We examined whether HDR mediates the relationship between a neurophysiological marker for interoception, heartbeat-evoked potential (HEP), and an index for cardiac reactivity, heart rate (HR) or heart rate variability (HRV). In a concurrent EEG-ECG experiment, 26 healthy participants underwent an emotion-evoking picture-evaluation task. Each of differential auditory cues was associated with certainty of the upcoming pleasant or unpleasant picture. The results showed attenuated HRV activity for certain cues in HDR individuals. Neural and physiological reactions to uncertain, unpleasant pictures were enhanced by depressive risk. These results suggest reduced responses in prediction processing and enlarged precision-weighted prediction error for unpleasant pictures. Finally, HDR significantly mediated the HEP and HR relationship for unexpected unpleasant stimuli. This study provides evidence that interoceptive predictive coding in the cardiac domain is altered by HDR and suggests that investigating brain-heart interactions related to predictive coding will offer insights into how to estimate depressive risk. Microbial colonization of tannin-rich tropical plants: interplay between degradability, methane production and tannin disappearance in the rumen AO_SCPLOWBSTRACTC_SCPLOWCondensed tannins in plants are found free and attached to protein and fibre but it is not known whether these fractions influence rumen degradation and microbial colonization. This study explored the rumen degradation of tropical tannins-rich plants and the relationship between condensed tannins fractions disappearance and microbial communities colonising plant particles. Leaves from Calliandra calothyrsus, Gliricidia sepium, and Leucaena leucocephala, pods from Acacia nilotica and the leaves of two agricultural by-products: Manihot esculenta and Musa spp. were incubated in situ in the rumen of dairy cows. An in vitro approach was also used to assess the effects of these plants on rumen fermentation parameters. All plants contained more than 100 g/kg of condensed tannins with a large proportion (32 to 61%) bound to proteins. Calliandra calothyrsus had the highest concentration of condensed tannins at 361 g/kg, whereas Acacia nilotica was particularly rich in hydrolysable tannins (350 g/kg). Hydrolysable and free condensed tannins from all plants completely disappeared after 24 h incubation in the rumen. Disappearance of protein-bound condensed tannins was variable with values ranging from 93% for Gliricidia sepium to 21% for Acacia nilotica. In contrast, fibre-bound condensed tannins disappearance averaged ~82% and did not vary between plants. Disappearance of bound fractions of condensed tannins was not associated with degradability of plant fractions. The presence of tannins interfered with the microbial colonisation of plants. Each plant had distinct bacterial and archaeal communities after 3 and 12 h of incubation in the rumen and distinct protozoal communities at 3 h. Adherent communities in tannin-rich plants had a lower relative abundance of fibrolytic microbes, notably Fibrobacter spp. Whereas, archaea diversity was reduced in high tannin-containing Calliandra calothyrsus and Acacia nilotica at 12 h of incubation. Concurrently, in vitro methane production was lower for Calliandra calothyrsus, Acacia nilotica and Leucaena leucocephala although for the latter total volatile fatty acids production was not affected and was similar to control. Here we show that the total amount of hydrolysable and condensed tannins contained in a plant govern the interaction with rumen microbes affecting degradability and fermentation. The effect of protein- and fibre-bound condensed tannins on degradability is less important. LabGym: quantification of user-defined animal behaviors using learning-based holistic assessment Quantifying animal behavior is important for many branches of biological research. Current computational tools for quantifying behaviors typically rely on a few pre-defined, simplified features to identify a behavior. However, such an approach underuses the behavioral information and restricts the tools applicability to limited behavior types or animal species. Here we report a new tool, LabGym, for quantifying animal behaviors without such limitations. Combining a novel approach for effective evaluation of animal motion with customizable convolutional recurrent networks for capturing spatiotemporal details, LabGym provides holistic behavioral assessment and accurately identify user-defined animal behaviors without restrictions on behavior types or animal species. It then calculates various parameters to provide quantitative measurements of the intensity and dynamics of each behavior. LabGym requires neither any intermediate step that causes information loss nor programming knowledge from users for post-hoc analysis. It tracks multiple animals simultaneously in various experimental settings for high-throughput and versatile analysis, and provides users a way to generate visualizable behavioral datasets that are valuable resources for research in neuroscience, behavioral science, and computer science. We demonstrate its efficacy in capturing subtle behavioral changes in animals ranging from soft-bodied invertebrates to mammals. Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition The teeth of sharks famously form a series of transversely-organised files with conveyor-belt replacement that are borne directly on the jaw cartilages, in contrast to the dermal plate-borne dentition of bony fishes that undergoes site-specific replacement. A major obstacle in understanding how this system evolved is the poorly understood relationships of the earliest chondrichthyans and the profusion of morphologically and terminologically diverse bones, cartilages, splints and whorls that they possess. Here we use tomographic methods to investigate mandibular structures in several early branching  acanthodian-grade stem-chondrichthyans. We show that the dentigerous jaw bones of disparate genera of ischnacanthids are united by a common construction, being growing bones with non-shedding dentition. Mandibular splints, which support the ventro-lateral edge of the Meckels cartilage in some taxa, are formed from dermal bone and may be an acanthodid synapomorphy. We demonstrate that the teeth of Acanthodopsis are borne directly on the mandibular cartilage and that this taxon is deeply nested within an edentulous radiation, representing an unexpected independent origin of teeth. Many or even all of the range of unusual oral structures may be apomorphic, but they should nonetheless be considered when building hypotheses of tooth and jaw evolution, both in chondrichthyans and more broadly. PIN-FORMED1 polarity in the shoot is insensitive to the polarity of neighbouring cells In plants, epidermal cells are planar-polarized along an axis marked by the asymmetric localization patterns of several proteins including PIN-FORMED1 (PIN1), which facilitates the directional efflux of the plant hormone auxin to pattern phyllotaxis (Heisler et al., 2010; Mansfield et al., 2018; Reinhardt et al., 2003). While PIN1 polarity is known to be regulated non-cell autonomously via the MONOPTEROS (MP) transcription factor, how this occurs has not been determined (Bhatia et al., 2016). Here we use mosaic expression of the serine threonine kinase PINOID (PID) to test whether PIN1 polarizes according to the polarity of neighbouring cells. Our findings reveal that PIN1 is insensitive to the polarity of PIN1 in neighbouring cells arguing against auxin flux or extracellular auxin concentrations acting as a polarity cue, in contrast to previous model proposals (Abley et al., 2016; Stoma et al., 2008).