bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors regulating translation coupled mRNA degradation in yeast
Authors: Basu Sudipto , Maiti Raktim , Hait Suman , Chaudhuri Ashesh Ray , Kundu Sudip 
Published: April 2022

AbstractCodon optimality is a major factor governing translation speed and efficiency, mRNA expression, and stability. Presence of optimal codons maintains a steady translation time, while non-optimal codons can lead to slow peptidyl transfer, prolonged translation time and frequent halt. Owing to this, there are co-translational surveillance protein complexes which monitor this process meticulously and can trigger the release of the mRNA transcript after prolonged halt. The surveillance protein complexes are closely associated with various degradation enzymes which can break down these released transcripts. Degradation of the released transcripts will be ensured if they contain amenable regions for enzyme binding and action. Here, we have used sequence data to estimate the relative abundance of non-optimal codons within a transcript. We found that transcripts with high relative abundance of non-optimal codon show reduced stability and half-life compared to transcripts with lower relative abundance of non-optimal codon. We have further integrated the relative abundance of non-optimal codons with internal unstructured segments for the transcripts, to show that the former is responsible for transcript release and the latter provides susceptible platform for endonuclease cleavage, which together is capable of destabilising and reducing the transcript’s half-life. Since this is a translational system, sequestration of mRNA into a dense forest of ribosomes is seen to prolong the transcript’s half-life even in the presence of destabilising factors.
GenMPI: Cluster Scalable Variant Calling for Short/Long Reads Sequencing Data
Authors: Ahmad Tanveer , Schuchart Joseph , Ars Zaid Al , Niethammer Christoph , Gracia José , Hofstee H. Peter 
Published: April 2022

ABSTRACTRapid technological advancements in sequencing technologies allow producing cost effective and high volume sequencing data. Processing this data for real-time clinical diagnosis is potentially time-consuming if done on a single computing node. This work presents a complete variant calling workflow, implemented using the Message Passing Interface (MPI) to leverage the benefits of high bandwidth interconnects. This solution (GenMPI) is portable and flexible, meaning it can be deployed to any private or public cluster/cloud infrastructure. Any alignment or variant calling application can be used with minimal adaptation. To achieve high performance, compressed input data can be streamed in parallel to alignment applications while uncompressed data can use internal file seek functionality to eliminate the bottleneck of streaming input data from a single node. Alignment output can be directly stored in multiple chromosome-specific SAM files or a single SAM file. After alignment, a distributed queue using MPI RMA (Remote Memory Access) atomic operations is created for sorting, indexing, marking of duplicates (if necessary) and variant calling applications. We ensure the accuracy of variants as compared to the original single node methods. We also show that for 300x coverage data, alignment scales almost linearly up to 64 nodes (8192 CPU cores). Overall, this work outperforms existing big data based workflows by a factor of two and is almost 20% faster than other MPI-based implementations for alignment without any extra memory overheads. Sorting, indexing, duplicate removal and variant calling is also scalable up to 8 nodes cluster. For pair-end short-reads (Illumina) data, we integrated the BWA-MEM aligner and three variant callers (GATK HaplotypeCaller, DeepVariant and Octopus), while for long-reads data, we integrated the Minimap2 aligner and three different variant callers (DeepVariant, DeepVariant with WhatsHap for phasing (PacBio) and Clair3 (ONT)). All codes and scripts are available at: https://github.com/abs-tudelft/gen-mpi
Anthropogenic disturbance impacts gut microbiome homeostasis in a Malagasy primate
Authors: Wasimuddin  , Malik Hina , Ratovonamana Yedidya R , Rakotondranary S Jacques , Ganzhorn Jörg U. , Sommer Simone 
Published: April 2022

AbstractIncreasing anthropogenic disturbances in Madagascar are exerting constrains on endemic Malagasy lemurs and their habitats, with possible effects on their health and survival. An important component of health is the gut microbiome, which might be disrupted by various stressors associated with environmental change. We have studied the gut microbiome of grey-brown mouse lemurs (Microcebus griseorufus), one of the smallest Malagasy primates and an important model of the convergent evolution of diseases. We sampled two sites: one situated in a national park and the other consisting of a more disturbed site around human settlement. We found that more intense anthropogenic disturbances indeed disrupted the gut microbiome of this lemur species with a marked reduction in its diversity and led to a shift in microbial community composition. Interestingly, we noted a decrease in beneficial bacteria together with a slight increase in disease-associated bacteria and alterations in microbial metabolic functions. Because of the crucial services provided by the microbiome to host health and in disease, such negative alterations in the gut microbiome of the lemur from anthropogenically disturbed habitat might impact the health of mouse lemurs, rendering them susceptible to diseases and ultimately affecting their survival in the shrinking biodiversity seen in Madagascar. Gut microbiome analyses might thus serve as an early warning signal for pending threats to lemur populations.
Sloppiness: fundamental study, new formalism and its application in model assessment
Authors: Jagadeesan Prem , Raman Karthik , Tangirala Arun K 
Published: April 2022

AbstractComputational modelling of biological processes poses multiple challenges in each stage of the modelling exercise. Some significant challenges include identifiability, precisely estimating parameters from limited data, informative experiments and anisotropic sensitivity in the parameter space. One of these challenges’ crucial but inconspicuous sources is the possible presence of large regions in the parameter space over which model predictions are nearly identical. This property, known as sloppiness, has been reasonably well-addressed in the past decade, studying its possible impacts and remedies. However, certain critical unanswered questions concerning sloppiness, particularly related to its quantification and practical implications in various stages of system identification, still prevail. In this work, we systematically examine sloppiness at a fundamental level and formalise two new theoretical definitions of sloppiness. Using the proposed definitions, we establish a mathematical relationship between the parameter estimates’ precision and sloppiness in linear predictors. Further, we develop a novel computational method and a visual tool to assess the goodness of a model around a point in parameter space by identifying local structural identifiability and sloppiness and finding the most sensitive and least sensitive parameters for non-infinitesimal perturbations. We demonstrate the working of our method in benchmark systems biology models of various complexities. The pharmacokinetic HIV infection model analysis identified a new set of biologically relevant parameters that can be used to control the free virus in an active HIV infection.Author summary
Neuroendocrine control of the proteostatic network by HPK-1 delays aging
Authors: Lazaro-Pena Maria I. , Diaz-Balzac Carlos A. , Das Ritika , Samuelson Andrew V. 
Published: April 2022

AbstractThe nervous system systemically coordinates proteostasis to delay organismal aging. However, the neuronal regulatory mechanisms that coordinate cellular anti-aging programs across tissue and cell-types are relatively unknown. In this work, we identify the homeodomain-interacting protein kinase (HPK-1), a transcriptional cofactor, as a novel neuronal component of the proteostatic network: its overexpression produces a paracrine signal to hyper-induce molecular chaperones and a neuroendocrine signal to induce autophagy in peripheral tissues. Neuronal HPK-1 signaling improves proteostasis in distal tissues through neurotransmitters. These pro-longevity modalities are independently regulated within serotonergic and GABAergic neurons, respectively, through distinct adaptive responses, either of which improve proteostasis in a cell non-autonomous manner. Serotonergic HPK-1 activity amplifies the heat shock response and protects the proteome from acute stress, without altering longevity. Conversely, increased GABAergic HPK-1 activity is sufficient to induce autophagy and extend longevity, without altering acute stress survival. Consistently, GABAergic neurons, but not serotonin, is essential for the cell non-autonomous induction of autophagy by neuronal HPK-1. These findings provide novel insight into how the nervous system partitions and coordinates unique adaptive response pathways to delay organismal aging, and reveals a key role for neuronal HPK-1 in regulating the proteostatic network throughout an intact metazoan animal.Significance StatementAging and the age-associated decline of the proteome is determined in part through neuronal control of evolutionarily conserved transcriptional effectors, which safeguard homeostasis under fluctuating metabolic and stress conditions by regulating an expansive proteostatic network in peripheral tissues. How neuronal signaling mechanisms are primed, relayed through an organism, and specific responses are initiated in receiving cell types remain poorly understood. We have discovered that the Caenorhabditis elegans homeodomain-interacting protein kinase (HPK-1) is a novel transcriptional effector that functions within two distinct neuronal cell-types to non-autonomously regulate divergent components of the proteostatic network to enhance stress resistance, improve proteostasis and delay aging.
Nuclear inhibitor of protein phosphatase 1 (NIPP1) regulates CNS tau phosphorylation and myelination during development
Authors: McKee Cody , Shrager Peter , Mazumder Arindam , Ganguly Archan , Mayer Abigail , Foley Karl , Ward Nancy , Youngman Margaret , Hou Hailong , Xia Houhui 
Published: April 2022

AbstractNuclear inhibitor of protein phosphatase 1 (NIPP1) is a known regulator of gene expression and has been shown to play roles in many physiological or pathological processes such as stem cell proliferation and skin inflammation. While NIPP1 has many regulatory roles in proliferating cells, its function in the central nervous system (CNS) has not been directly investigated. In the present study, we examined NIPP1 CNS function using a conditional knockout (cKO) mouse model, in which the nipp1 gene is excised from neural precursor cells. These mice demonstrate severe developmental impairments that lead to premature lethality within the first few postnatal weeks. To delineate some of the neurological changes occurring in these animals, we first assessed microtubule associated protein tau, a known target of NIPP1 activity. Furthermore, observed tremors prompted exploration of myelin integrity, an integral structure for CNS function, whose disruption is associated with various neurological disorders and neurodegenerative diseases. First, immunoblotting demonstrated increased phospho-tau and altered AKT and PP1 activity in NIPP1 cKO mice, suggesting increased tau phosphorylation likely results from a shift in kinase/phosphatase activity. Second, immunoblots, electron microscopy, and electrophysiology demonstrated a myelin deficit within the brain and optic nerve. Our study suggests that NIPP1 in neural precursors regulates phosphorylation of tau and CNS myelination and may represent a novel therapeutic target for neurodegenerative diseases.
Retinal aging transcriptome and cellular landscape in association with the progression of age-related macular degeneration
Authors: Wang Jiang-Hui , Wong Raymond C.B. , Liu Guei-Sheung 
Published: April 2022

AbstractAge is the main risk factor for age-related macular degeneration (AMD), a leading cause of blindness in the elderly, with limited therapeutic options. Here we systematically analyzed the transcriptomic characteristics and cellular landscape of the aging retina from controls and patients with AMD. We identify the aging genes in the retina that are associated with innate immune response and inflammation. Deconvolution analysis reveals that the estimated proportion of M2 and M0 macrophages is increased and decreased, respectively with both age and AMD severity. Moreover, we find that Müller glia are increased with age but not with disease severity. Several genes associated with both age and disease severity in AMD, particularly C1s and MR1, are strong positively correlated with the proportions of Müller glia. Our studies expand the genetic and cellular landscape of AMD and provide avenues for further studies on the relationship between age and AMD.
DeepVelo: Deep Learning extends RNA velocity to multi-lineage systems with cell-specific kinetics
Authors: Cui Haotian , Maan Hassaan , Taylor Michael D. , Wang Bo 
Published: April 2022

1AbstractThe introduction of RNA velocity in single-cell studies has opened new ways of examining cell differentiation and tissue development. Existing RNA velocity estimation methods rely on strong assumptions of predefined dynamics and cell-agnostic constant transcriptional kinetic rates, which are often violated in complex and heterogeneous single-cell RNA sequencing (scRNA-seq) data. To overcome these limitations, we propose DeepVelo, a novel method that estimates thecell-specificdynamics of splicing kinetics using Graph Convolution Networks (GCNs). DeepVelo generalizes RNA velocity to cell populations containing time-dependent kinetics and multiple lineages, which are common in developmental and pathological systems. We applied DeepVelo to disentangle multifaceted kinetics in the processes of dentate gyrus neurogenesis, pancreatic endocrinogenesis, and hindbrain development. The method infers time-varying cellular rates of transcription, splicing and degradation, recovers each cell’s stage in the underlying differentiation process, and detects functionally relevant driver genes regulating these processes. DeepVelo relaxes the constraints of previous techniques, facilitates the study of more complex differentiation and lineage decision events in heterogeneous scRNA-seq data, and is more computationally efficient than previous techniques.
Illuminating kinase inhibitors biology by cell signaling profiling
Authors: Medvedev Alexander V. , Makarov Sergei , Medvedeva Lyubov A. , Martsen Elena , Gorman Kristen L. , Lin Benjamin , Makarov Sergei S. 
Published: April 2022

ABSTRACTProtein kinase inhibitors (PKI) are promising drug candidates for many diseases. However, even selective PKIs interact with multiple kinases and non-kinase targets. Existing technologies detect these interactions but not the resultant biological effects. Here, we describe an orthogonal PKI evaluation approach that entails fingerprinting of cell signaling responses. As the readout, we profiled the activity of 45 transcription factors linking signaling pathways to genes. We found that inhibitors of the same kinase family exhibited a consensus TF activity profile (TFAP) invariant to PKI chemistry and mode of action (allosteric, ATP-competitive, or genetic). Specific PKI consensus signatures were found for multiple kinase families (Akt, CDK, Aurora, RAF, MEK, and ERK) with high-similarity consensus signatures of signaling cascade kinases. Thus, the PKI consensus signatures provide bona fide markers of cell response to on-target PKI activity. However, the consensus signatures appeared only at certain inhibitor concentrations (‘on-target windows’). Using concentration-response signature analysis, we identified PKI interactions dominating cell response at other concentrations. Finally, we illustrate this approach by selecting putative chemical probes for evaluated kinases. Therefore, the effect-based TFAP approach illuminates PKI biology invisible to target-based technologies and provides clear quantitative metrics to aid the selection of polypharmacological PKIs as chemical probes and drug leads.
Behavioral dysregulation and monoaminergic deficits precede memory impairments in human tau-overexpressing (htau) mice
Authors: Khan Kanza M. , Selvakumar Govindhasamy Pushpavathi , Balasubramanian Nagalakshmi , Wang Ruixiang , Pierson Samantha , Hefti Marco , Marcinkiewcz Catherine A 
Published: April 2022

ABSTRACTAlzheimer’s disease (AD) poses an ever-increasing public health concern as the population ages, affecting more than 6 million Americans. AD patients present with mood and sleep changes in the prodromal stages that may be partly driven by loss of monoaminergic neurons in brainstem, but a causal relationship has not been firmly established. The goal of the present study was to evaluate depressive and anxiety-like behaviors in a mouse model of human tauopathy (htau mice) at 4 and 6 months of age prior to the onset of cognitive impairments and correlate these behavior changes with tau pathology, neuroinflammation, and monoaminergic dysregulation in the DRN and LC. We observed depressive-like behaviors at 4 months of age in male and female htau mice and hyperlocomotion in male htau mice. At 6 months, male htau mice developed anxiety-like behavior in the EZM, whereas hyperlocomotion had resolved by this time point. Depressive-like behaviors in the social interaction test persisted at 6 months but were resolved in the sucrose preference test. There was also a significant reduction in number and density of 5-HT-immunoreactive neurons in the rostral DRN in htau mice at 4 months and 5-HT neuronal density was negatively correlated with the intensity of phosphorylated tau staining in this subregion. Additionally, we found evidence of microglial activation in the mid and caudal DRN and astrocytic activation in the rostral DRN. 5-HT neuronal activity was reduced in the DRN and accompanied by downregulation of Tph2 and Sert, whereas genes that promote neuroinflammation and tau phosphorylation were upregulated. Finally, there was enhanced ptau202/205 staining and microglial activity in the LC of htau mice and reduced TH optical density, although the number and density of TH+ neurons were not altered. In total, these results suggest that tau pathology in the DRN and the resulting loss of serotonergic neurotransmission may drive depressive-like behaviors in the early stages of AD, whereas anxiety-like behaviors develop later and may result from neurodegeneration in other regions.
Gut Enterochromaffin Cells are Critical Drivers of Visceral Pain and Anxiety
Authors: Bayrer James R. , Castro Joel , Venkataraman Archana , Touhara Kouki K. , Rossen Nathan D. , Morrie Ryan D. , Hendry Aenea , Madden Jessica , Braverman Kristina N. , Schober Gudrun , Brizuela Mariana , Silva Carla Bueno , Ingraham Holly A. , Brierley Stuart M. , Julius David 
Published: April 2022

ABSTRACTGastrointestinal (GI) discomfort is a hallmark of most gut disorders and represents a significant component of chronic visceral pain 1. For the growing population afflicted by irritable bowel syndrome (IBS), GI hypersensitivity and pain persist long after signs of tissue injury have resolved 2. IBS also exhibits a strong sex bias afflicting women three-fold more than men 1. Identifying the molecules, cells, and circuits that mediate both the acute and persistent phases of visceral pain is a critical first step in understanding how environmental and endogenous factors produce long-term changes in the nervous system or associated tissues to engender chronic pain syndromes 3,4. Enterochromaffin (EC) cells within the gut epithelium are exceedingly rare sensory neuroendocrine cells that detect and transduce noxious stimuli to nearby nerve endings via serotonin. Here, we manipulate murine EC cell activity using genetic strategies to ascertain their contributions to visceral pain. We show that acute EC cell activation is sufficient to elicit hypersensitivity to gut distension and necessary for the sensitizing actions of isovalerate, a bacterially derived short-chain fatty acid irritant associated with inflammatory GI disorders. Remarkably, prolonged EC cell activation by itself is sufficient to produce persistent visceral hypersensitivity, even in the absence of an instigating inflammatory episode. Perturbing the activity of these rare EC cells led to a marked increase in anxiety-like behaviors that normalized after blocking serotonergic signaling. Sex differences were also observed accross a range of assays indicating that females have a higher baseline visceral sensitivity. Our findings validate a critical role for EC cell-mucosal afferent signaling in acute and persistent GI pain while highlighting mechanistically defined genetic models for studying visceral hypersensitivity, sex differences, and associated behaviors.
Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using an SVM classifier
Authors: Tripathi Utkarsh , Mizrahi Liron , Alda Martin , Falkovich Gregory , Stern Shani 
Published: April 2022

AbstractBipolar disorder (BD) is a mood disorder with a high morbidity and death rate. Lithium (Li), a prominent mood stabilizer, is fully effective in roughly 30% of BD patients. The remaining patients respond partially or do not respond at all. Another drug used to treat BD patients is valproate (VPA). Plenty of efforts has been made to understand how these drugs affect the patients’ neurons. We have performed electrophysiological recordings in patient-derived dentate gyrus (DG) granule neurons for three groups: control individuals, BD patients who respond to Li treatment (LR), and BD patients who do not respond to Li treatment (NR). The recordings were analyzed by the statistical tools of modern information theory, which enabled us to recognize new relationships between the electrophysiological features. These added features included the entropy of several electrophysiological measurements and the mutual information between different types of electrophysiological measurements. Information theory features provided further knowledge about the distribution of the electrophysiological entities, which improved basic classification schemes. These newly added features enabled a significant improvement in our ability to distinguish the BD patients from the control individuals (an improvement from 60% accuracy to 74% accuracy) and the Li responders from the non-responders in the BD population using Support Vector Machine (SVM) classification algorithms (an improvement from 81% accuracy to 99% accuracy). These new tools showed that LR neurons are less distinguishable from control neurons after Li treatment but not after VPA treatment, whereas NR neurons become more distinguishable from control neurons after Li treatment.
Single-cell multimodal modeling with deep parametric inference
Authors: Hu Huan 
Published: April 2022

AbstractThe paired measurement of multiple modalities, known as the multimodal analysis, is an exciting frontier for connecting single-cell genomics with epitopes and functions. Mapping of transcriptomes in single-cells and the integration with cell phenotypes enable a better understanding of cellular states. However, assembling these paired omics into a unified representation of the cellular state remains challenging with the unique technical characteristics of each measurement. In this study, we built a deep parameter inference model (DPI) based on the properties of single-cell multimodal data. DPI is a complete single-cell multimodal omics analysis framework, which has built in multimodal data preprocessing, multimodal data integration, multimodal data reconstruction, reference and query, disturbance prediction and other analysis functions.