bluuebunny/crossref_metadata_2025 huggingface dataset:
Reconstructing Langevin systems from high and low-resolution time series using Euler and Hermite reconstructions
Authors: Arani Babak M. S. , Carpenter Stephen R. , van Nes Egbert H.
Published: June 2024

The ecological literature often features phenomenological dynamic models lacking robust validation against observational data. Reverse engineering is an alternative approach, where time series data are utilized to infer or fit a stochastic differential equation. This process, known as system reconstruction, presents significant challenges. This paper addresses the estimation of the (often) non-linear deterministic and stochastic parts of Langevin models, one of the simplest yet commonly used stochastic differential equations. We introduce a Maximum Likelihood Estimation (MLE) inference method, termed Euler reconstruction, tailored for time series data with medium to high resolution. However, the Euler approach is not reliable for low-resolution data. To fill the gap for sparsely sampled data, we present an MLE inference method pioneered by Aït-Sahalia, that we term Hermite reconstruction. We employ a powerful modeling framework utilizing splines to detect inherent nonlinearities in the unknown data-generating system to achieve high accuracy with minimal computational burden. Applying Euler and Hermite reconstructions to a range of simulated, ecological, and climate datasets, we demonstrate their efficacy and versatility. We provide a user-friendly tutorial and a MATLAB package called the ‘MATLAB reconstruction package’.
Direct β1/ β2 AMPK activation reduces liver steatosis but not fibrosis in a mouse model of non-alcoholic steatohepatitis
Authors: Mather Karly M. , Boland Michelle L. , Rivers Emma L. , Srivastava Abhishek , Schimpl Marianne , Hemsley Paul , Robinson James , Wan Paul T. , Hansen Josefine , Read Jon A. , Trevaskis James L. , Smith David M.
Published: June 2024

5’AMP-activated protein kinase (AMPK) activators show potential for treating Non-Alcoholic Fatty Liver Disease (NAFLD) and Non-Alcoholic Steatohepatitis (NASH) due to their inhibiting effects on fatty acid and cholesterol synthesis. The absence of treatments for NASH, and its propensity for progression to severe disease, lead us to identify and characterize BI9774, a small molecule AMPK activator, which we used to evaluate this potential, including its ability to reduce the NASH specific qualities of fibrosis and inflammation in a preclinical study.

MaleLepob/Lepobmice on a control or NASH inducing (AMLN) diet, with liver fibrosis were given BI9774 or vehicle for 6 weeks while metabolic and NASH endpoints were evaluated.

BI9774 treatment decreased plasma ALT, terminal liver weight, and liver lipids. RNA expression of collagen-related genes decreased, although collagen protein and inflammation remained unaltered. We also observed increased heart weight and glycogen levels, and increased expression of genes associated with cardiac hypertrophy.

AMPK activation improved many metabolic endpoints, but lack of significant improvement in liver fibrosis and negative cardiac effects suggest systemic AMPK activation is not an ideal NASH therapy. Reductions in steatosis and fibrosis-related genes indicate that, with extended treatment, a liver specific AMPK activator has potential to resolve hepatic fibrosis.

Summary Statement

Fatty liver disease affects up to 30 percent of adults worldwide with 30% of patients progressing to more sever liver disease. AMPK activation can help reduce liver fat.
A bioinformatic approach for the prediction and functional classification ofToxoplasma gondiilong non-coding RNAs
Authors: Vanagas Laura , Cristaldi Constanza , Bella Gino La , Ganuza Agustina , Angel Sergio O. , Alonso Andrés M.
Published: June 2024

Long non-coding RNAs (lncRNAs) have emerged as significant players in diverse cellular processes, including cell differentiation. Advancements in computational methodologies have facilitated the prediction of lncRNA functions, enabling insights even in non-model organisms like pathogenic parasites, in roles such as parasite development, antigenic variation, and epigenetics. In this work, we focus on the apicomplexanToxoplasma gondiidifferentiation process, where the infective stage, tachyzoite, can develop into the cysted stage, bradyzoite, under stress conditions. Using a publicly available transcriptome dataset, we predicted lncRNA sequences associated with this differentiation process. Notably, a substantial proportion of these predicted lncRNAs exhibited stage-specific expression, particularly at the bradyzoite stage. Furthermore, co-expression patterns between coding transcripts and lncRNAs suggest their involvement in shared processes, such as bradyzoite development. TglncRNA loci analysis revealed their potential influence on the expression of nearby coding genes, including subtelomeric genes unique to theT. gondiigenome. Finally, with a k-mer analysis approach, we identified functional relationships between characterized lncRNAs from model organisms likeHomo sapiensand predictedT. gondiilncRNAs. Our perspective led to the identification of aT. gondiilncRNA potentially mediating DNA damage repair pathways, shedding light on the adaptive mechanisms ofT. gondiiin response to stress conditions.
Genomic introgression between critically endangered and stable species of Darwin’s tree finches on the Galapagos Islands
Authors: Dudaniec Rachael Y. , Yadav Sonu , Catchen Julian , Kleindorfer Sonia
Published: June 2024

Natural hybridisation among rare or endangered species and stable congenerics is increasingly topical for the conservation of species-level diversity under anthropogenic impacts. Evidence for beneficial genes being introgressed into or selected for in hybrids raises concurrent questions about its evolutionary significance. In Darwin’s tree finches on the island of Floreana (Galapagos Islands, Ecuador), the Critically Endangered medium tree finch (Camarhynchus pauper) undergoes introgression with the stable small tree finch (Camarhynchus parvulus), and hybrids regularly backcross withC. parvulus.Earlier studies in 2005-2013 documented an increase in the frequency ofCamarhynchushybridisation on Floreana using field-based and microsatellite data. With single nucleotide polymorphism (SNP) data from the same Floreana tree finches sampled in 2005 and 2013 (n = 95), we examine genome-wide divergence across parental and hybrid birds and evidence for selection in hybrids. In assessing previous estimates of introgression we found that just 18% of previously assigned hybrid birds based on microsatellites were assigned to hybrids using SNPs. Over half of the previously assigned hybrids (63%) were reassigned toC. parvulus,though parental species showed concordance with prior assignments. Of 4869 private alleles found in hybrid birds, 348 were at a high frequency (≥0.30) that exceeded their parental species of origin 89-96% of the time. Across the two years, 3436 (70.6%) private alleles underwent a substantial (≥0.30) allele frequency increase or decrease. Of these, 28 private alleles were identified as candidate loci under selection via local PCA genome scans and outlier tests.

Alleles were annotated to genes associated with inflammation, immunity, brain function and development. We provide evidence that introgression among a critically endangered and stable species of Darwins’ tree finch is being retained by selection across years and may aid in the retention of genetic diversity in birds threatened with extinction.
Extracellular Respiration is a Latent Energy Metabolism inEscherichia coli
Authors: Kundu Biki Bapi , Krishnan Jayanth , Szubin Richard , Patel Arjun , Palsson Bernhard O. , Zielinski Daniel C. , Ajo-Franklin Caroline M.
Published: June 2024

Diverse microbial species utilize redox shuttles to exchange electrons with their environment through mediated extracellular electron transfer (EET). This process maintains redox homeostasis and supports anaerobic survival across diverse microbial communities. Although mediated EET has been extensively leveraged for bioelectrocatalysis and bioelectronics for decades, fundamental questions remain about how these redox shuttles are reduced within cells and their bioenergetic implications. This knowledge gap limits our understanding of the physiological roles of mediated EET in various microbes and hampers the development of efficient microbial electrochemical technologies. To address this, we developed a methodology integrating genome editing, electrochemistry, and systems biology to investigate the mechanism and bioenergetic implications of mediated EET in bacteria. Using this approach, we uncovered a mediated EET mechanism inEscherichia coli. In the absence of alternative electron sinks, the redox cycling of 2-hydroxy-1,4-naphthoquinone (HNQ) via a cytoplasmic nitroreductase enabledE. colito respire and grow on an extracellular electrode. Genome-scale metabolic modeling suggested that HNQ- mediated EET offers a more energetically favorable route for supporting anaerobic growth than canonical fermentation. Transcriptome analysis revealed redox perturbations in response to HNQ and identified rapid metabolic adaptations that support growth. This work demonstrates thatE. colican grow independently of classical electron transport chains and fermentative pathways, unveiling a new type of anaerobic energy metabolism.
Automation Bias in AI-Assisted Detection of Cerebral Aneurysms on Time-of-Flight MR-Angiography
Authors: Kim Su Hwan , Schramm Severin , Riedel Evamaria Olga , Schmitzer Lena , Rosenkranz Enrike , Kertels Olivia , Bodden Jannis , Paprottka Karolin , Sepp Dominik , Renz Martin , Kirschke Jan , Baum Thomas , Maegerlein Christian , Boeckh-Behrens Tobias , Zimmer Claus , Wiestler Benedikt , Hedderich Dennis M.
Published: June 2024

Background

AI systems have the potential to support in detecting cerebral aneurysms. Yet, the role of automation bias (inclination of humans to overly rely on automated decision-making systems) in AI-assisted cerebral aneurysm detection remains unclear.

Purpose

To determine how automation bias can affect radiologists with varying experience levels when reading time-of-flight magnetic resonance angiography (TOF-MRA) studies with the assistance of an AI system for cerebral aneurysm detection.

Methods

In this prospective experiment, nine radiologists with varying levels of experience evaluated twenty TOF-MRA exams for the presence of anterior circulation aneurysms, with each arterial segment rated on a 4-point Likert scale, and provided follow-up recommendations. Every case was evaluated twice (with or without assistance by the AI software © mdbrain), with a washout-period of at least four weeks between the two sessions. Ten out of twenty cases included at least one false-positive AI finding. Aneurysm ratings, follow-up recommendations, and reading times were assessed using the Wilcoxon signed-rank test. A thematic analysis was performed to summarize reader feedback and observations.

Results

False-positive AI results led to significantly higher suspicion of aneurysm findings (p = 0.01). Inexperienced readers further recommended significantly more aggressive follow-up examinations when presented with false-positive AI findings (p = 0.005). Reading times were significantly shorter with AI assistance in inexperienced (164.1 vs 228.2 seconds; p < 0.001), moderately experienced (126.2 vs 156.5 seconds; p < 0.009), and very experienced (117.9 vs 153.5 seconds; p < 0.001) readers alike.

Conclusion

Our results demonstrate susceptibility of radiology readers to automation bias in detecting cerebral aneurysms in TOF-MRA studies when encountering false-positive AI findings. In inexperienced readers, this behavior further translated into more aggressive follow-up recommendations. AI assistance resulted in significantly shorter reading times across experience levels. While AI systems for cerebral aneurysm detection can provide benefits, challenges in human-AI interaction need to be mitigated to ensure safe and effective adoption.
Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning
Authors: Mehdi Rana Raza , Kadivar Nikhil , Mukherjee Tanmay , Mendiola Emilio A. , Shah Dipan J. , Karniadakis George , Avazmohammadi Reza
Published: June 2024

Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, we develop a completely non-invasive methodology that identifies the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, we demonstrate the remarkable performance of a multi-fidelity ML model that combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. Our results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in-vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.
Protein Coronas Derived from Cerebrospinal Fluid Enhance the Interactions Between Nanoparticles and Brain Cells
Authors: Morshed Nabila , Rennie Claire , Faria Matthew , Collins-Praino Lyndsey , Care Andrew
Published: June 2024

Neuronanomedicine harnesses nanoparticle technology for the treatment of neurological disorders. An unavoidable consequence of nanoparticle delivery to biological systems is the formation of a protein corona on the nanoparticle surface. Despite the well-established influence of the protein corona on nanoparticle behavior and fate, as well as FDA approval of neuro-targeted nanotherapeutics, the effect of a physiologically relevant protein corona on nanoparticle-brain cell interactions is insufficiently explored. Indeed, less than 1% of protein corona studies have investigated protein coronas formed in cerebrospinal fluid (CSF), the fluid surrounding the brain. Herein, we utilize two clinically relevant polymeric nanoparticles (PLGA and PLGA-PEG) to evaluate the formation of serum and CSF protein coronas. LC-MS analysis revealed distinct protein compositions, with selective enrichment/depletion profiles. Following incubation with brain cells, serum and CSF coronas on PLGA particles showed enhanced associations with all cell types as compared to their corresponding corona on PLGA-PEG particles. CSF-derived protein coronas on PLGA nanoparticles, specifically, showed the greatest nanoparticle-cell interactions, with Pearson’s correlation analysis revealing that proteins associated with enhanced nanoparticle-cell interactions were exclusively enriched in this protein corona. This study demonstrates the importance of correct choice of physiologically relevant biological fluids, and its influence on the formation of the protein corona, subsequent nanoparticle-cell interactions.
Natural killer cell migration in HIV-infected individuals is inhibited by impairment of HIF-1α-mediated glycolysis
Authors: Yu Xiaowen , Zhou Jie , Lei Jie , Ge Hongchi , Zhang Zining , Fu Yajing , Han Xiaoxu , Hu Qinghai , Ding Haibo , Geng Wenqing , Shang Hong , Jiang Yongjun
Published: June 2024

Natural killer (NK) cells serve as the first line of defense of the immune system and play a crucial role in fighting against HIV infection. The effective function of NK cells is closely related to their migration ability, but the status of NK cell migration in HIV-infected individuals and the regulation mechanism for NK cell migration remains unknown. Here, we found that NK cell migration was significantly impaired in HIV-infected individuals, lower in immune non-responders (INRs) compared with immune responders (IRs), and positively correlated with CD4+T cell counts. Further investigations showed that the decreased NK cell migration in HIV infection was caused by the impairment of glycolysis. Mechanistically, we found that NK cell migration was regulated by HIF-1α pathway, and inhibitory receptor TIGIT restrained HIF-1α expression by inhibiting PI3K/AKT/mTORC1 or ERK signaling pathway, consequently weakening the glycolysis of NK cells in HIV-infected individuals, and ultimately leading to down-regulation of migration. Collectively, we uncovered a mechanism of reduced NK cell migration in HIV infection and provided a new insight for immunotherapy in HIV infection.

In Brief

The effective function of NK cells is closely related to its migration ability. The authors show that impaired NK cell migration in HIV-infected individuals is caused by TIGIT inhibiting HIF-1α-mediated glycolysis via PI3K/AKT/mTORC1 or ERK pathway.
Effective Inclusion of Electronic Polarization Improves the Description of Electrostatic Interactions: The prosECCo75 Biomolecular Force Field
Authors: Nencini Ricky , Tempra Carmelo , Biriukov Denys , Riopedre-Fernandez Miguel , Cruces Chamorro Victor , Polak Jakub , Mason Philip E. , Ondo Daniel , Heyda Jan , Ollila O. H. Samuli , Jungwirth Pavel , Javanainen Matti , Martinez-Seara Hector
Published: June 2024

prosECCo75 is an optimized force field effectively incorporating electronic polarization via charge scaling. It aims to enhance the accuracy of nominally nonpolarizable molecular dynamics (MD) simulations for interactions in biologically relevant systems involving water, ions, proteins, lipids, and saccharides. Recognizing the inherent limitations of nonpolarizable force fields in precisely modeling electrostatic interactions essential for various biological processes, we mitigate these shortcomings by accounting for electronic polarizability in a physical rigorous mean-field way that does not add to computational costs. With this scaling of (both integer and partial) charges within the CHARMM36 framework, prosECCo75 addresses overbinding artifacts. This improves agreement with experimental ion binding data across a broad spectrum of systems - lipid membranes, proteins (including peptides and amino acids), and saccharides - without compromising their biomolecular structures. prosECCo75 thus emerges as a computationally efficient tool providing enhanced accuracy and broader applicability in simulating the complex interplay of interactions between ions and biomolecules, pivotal for improving our understanding of many biological processes.
Inequality, heterogeneity, and chance: Multiple factors and their interactions
Authors: Caswell Hal , van Daalen Silke F.
Published: June 2024

A heterogeneous population is a mixture of groups differing in vital rates. In such a population, some of the variance in demographic outcomes (e.g., longevity, lifetime reproduction) is due to heterogeneity and some is the result of stochastic demographic processes. Many studies have partitioned variance into its between-group and within-group components, but have focused on single factors. Especially for longevity, variance due to stochasticity is far greater than that due to heterogeneity. Here we extend analysis to multiple-factor studies, making it possible to calculate the contributions to variance of each factor and each of the interactions among factors. We treat the population as a mixture and use the marginal mixing distributions to compute variance components. Examples are presented: longevity as a function of sex, race, and U.S. state of residence, lifetime reproduction among set of developed countries and as a result of resource availability and pesticide exposure.
Stick-slip unfolding favors self-association of expandedHTTmRNA
Authors: O’Brien Brett M. , Moulick Roumita , Jiménez-Avalos Gabriel , Rajasekaran Nandakumar , Kaiser Christian M. , Woodson Sarah A.
Published: June 2024

In Huntington’s Disease (HD) and related disorders, expansion of CAG trinucleotide repeats produces a toxic gain of function in affected neurons. Expandedhuntingtin(expHTT) mRNA forms aggregates that sequester essential RNA binding proteins, dysregulating mRNA processing and translation. The physical basis of RNA aggregation has been difficult to disentangle owing to the heterogeneous structure of the CAG repeats. Here, we probe the folding and unfolding pathways of expHTTmRNA using single-molecule force spectroscopy. Whereas normalHTTmRNAs unfold reversibly and cooperatively, expHTTmRNAs with 20 or 40 CAG repeats slip and unravel non-cooperatively at low tension. Slippage of CAG base pairs is punctuated by concerted rearrangement of adjacent CCG trinucleotides, trapping partially folded structures that readily base pair with another RNA strand. We suggest that the conformational entropy of the CAG repeats, combined with stable CCG base pairs, creates a stick-slip behavior that explains the aggregation propensity of expHTTmRNA.
Pulse Instabilities Can Shape Virus-Immune Co-evolution
Authors: Kessler David A. , Levine Herbert
Published: June 2024

Adaptive immune systems engage in an arms race with evolving viruses, trying to generate new responses to viral strains that continually move away from the set of genetically-varying strains that have already elicited a functional immune response. It has been argued that this dynamical process can lead to a propagating pulse of an ever-changing viral population and concomitant immune response. Here, we introduce a new stochastic model of viral-host co-evolution, taking into account finite-sized host populations and varying processes of immune “forgetting”. Using both stochastic and determinstic calculations, we show that there is indeed a possible pulse solution, but for a large host population size and for finite memory capacity, the pulse becomes unstable to the generation of new infections in its wake. This instability leads to an extended endemic infection pattern, demonstrating that the population-level behavior of virus infections can exhibit a wider range of behavior than had been previously realized.
OneSC: A computational platform for recapitulating cell state transitions
Authors: Peng Da , Cahan Patrick
Published: June 2024

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high- resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico ‘synthetic’ cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.
The joint evolution of separate sexes and sexual dimorphism
Authors: Lesaffre Thomas , Pannell John R. , Mullon Charles
Published: June 2024

Dioecious plants are frequently sexually dimorphic. Such dimorphism, which reflects responses to selection acting in opposite directions for male and female components of fitness, is commonly thought to emerge after separate sexes evolved from hermaphroditism. But associations between allocation to male and female function and traits under sexual conflict may well also develop in hermaphroditic ancestors. Here, we show that variation in sex allocation and a trait under sexual conflict inevitably generates an advantage to sexual specialisation, fueling the transition to dioecy. In the absence of constraints, this leads to the joint evolution of separate sexes and sexual dimorphism through the build-up of an association between sex allocation and the conflict trait, such that eventually the population consists of unisexuals expressing their sex-specific optima. We then investigate how such association might materialise genetically, either via recombination suppression or via sex-dependent expression, and show that the genetic architecture of sex allocation and the conflict trait readily evolves to produce the association favoured by selection. Finally and in agreement with previous theory, we demonstrate that limited dispersal and self-fertilisation, which are pervasive ecological characteristics of flowering plants, can offset the advantage of sexual specialisation generated by sexual conflict and thus maintain hermaphroditism. Taken together, our results indicate that advantages to sexual specialisation are inevitable when there is conflict between sexual functions in hermaphrodites, but these advantages can be counterbalanced by ecological benefits of hermaphroditism.
Decoding chromosome organization using CheC-PLS: chromosome conformation by proximity labeling and long-read sequencing
Authors: Xu Kewei , Zhang Yichen , Baldwin-Brown James , Sasani Thomas A. , Phadnis Nitin , Miller Matthew P. , Rog Ofer
Published: June 2024

Genomic approaches have provided detailed insight into chromosome architecture. However, commonly deployed techniques do not preserve connectivity-based information, leaving large-scale genome organization poorly characterized. Here, we developed CheC-PLS: a proximity-labeling technique that indelibly marks, and then decodes, protein-associated sites. CheC-PLS tethers dam methyltransferase to a protein of interest, followed by Nanopore sequencing to identify methylated bases - indicative ofin vivoproximity - along reads >100kb. As proof-of-concept we analyzed, in budding yeast, a cohesin-based meiotic backbone that organizes chromatin into an array of loops. Our data recapitulates previously obtained association patterns, and, importantly, exposes variability between cells. Single read data reveals cohesin translocation on DNA and, by anchoring reads onto unique regions, we define the internal organization of the ribosomal DNA locus. Our versatile technique, which we also deployed on isolated nuclei with nanobodies, promises to illuminate diverse chromosomal processes by describing thein vivoconformations of single chromosomes.
SpaNorm: spatially-aware normalisation for spatial transcriptomics data
Authors: Salim Agus , Bhuva Dharmesh D , Chen Carissa , Yang Pengyi , Davis Melissa J , Yang Jean YH
Published: June 2024

Library size normalisation is necessary to enable comparisons between observations in transcriptomic datasets. Numerous methods have been developed to normalise these effects with sample and gene specific adjustments. However, in spatial transcriptomics data, normalisation is complicated by the fact that spatial region-specific library size confounds biology. The most popular approach of adapting methods developed for single-cell RNA-seq data has been shown to excessively remove biological signals associated with spatial domains and thus results in poorer downstream domain identification. To this end, we propose the first spatially-aware normalisation method, SpaNorm. SpaNorm concurrently models spatial library size effects and the underlying smooth biology, to tease apart these effects, and thereby remove library size effects without removing biology. This is achieved through optimal decomposition of spatially smooth variation into those related and unrelated to library size and the use of location-specific scaling factors. Using 27 tissue samples from 6 datasets spanning 4 spatial platforms, we show that SpaNorm outperforms current state of the art methods at retaining biological information in the form of spatial domains and spatially variable genes (SVGs) better than 4 commonly used single-cell normalisation approaches. SpaNorm is versatile and it can be used for both spot-based and subcellular spatial transcriptomics data. Notably, the benefit of using SpaNorm is more pronounced for the latter data such as those from Xenium, STOmics and CosMx platforms for which the proportion of genes exhibiting region-specific library size effect is higher. SpaNorm works equally well with segmented cell-level data and spot-based data where each spot contains multiple cells.
Genetic Ethnicity and Hypertension Epistatic Interaction Underlying Racial Disparities in US Multiple Myeloma Susceptibility
Authors: Dumont Emmanuel LP , Han Luke , Kodali Srisundesh , Aptekmann Ariel , Carter-Bawa Lisa , Feinman Rena , Tycko Benjamin , Siegel David S. , Goy Andre , Kaplan Peter , Do Catherine
Published: June 2024

Background

Multiple myeloma (MM), a malignant plasma cell disorder, exhibits pronounced racial disparities in incidence and patient outcomes. The Centers for Disease Control and Prevention (CDC) reports that MM is twice as common in Black Americans as in White Americans. Understanding these racial disparities is paramount to addressing potential healthcare biases and developing targeted interventions to ensure equitable patient care and outcomes.

Methods

Using the ‘All of Us’ database from the National Institute of Health, we performed a retrospective study on 413,457 participants. Of these, 1,430 were diagnosed with MM. We examined the factors contributing to racial disparities in MM risk using multivariable statistical analysis, including interaction effects.

Results

To comprehensively account for the multidimensional aspects of self-reported race followed by the CDC, we incorporated genetic ethnicity, demographics (age, gender), body mass index, social determinants of health (zipcode’s deprivation index, and health insurance status), and common pre-existing comorbidities (hypertension, diabetes, congestive heart failure - CHF, and chronic obstructive pulmonary disease) into our analysis. Our findings reveal that the racial disparities in health outcomes between non-Hispanic Black and non-Hispanic White individuals, as reported by the CDC, are driven by a synergistic epistatic interaction between having African as a predominant genetic ethnicity and being diagnosed or treated for hypertension (OR: 2.92, 95% CI: 1.54 to 5.57, P = 0.001). This interaction is also true for individuals whose primary genetic ancestry is Ad Mixed American (OR: 2.31, 95% CI: 1.02 to 5.2, P = 0.044). The other variables significantly associated with MM risk are having a predominant genetic ancestry of Ad Mixed American (OR: 0.41, 95% CI: 0.2 to 0.85, P = 0.017), the lack of health insurance (OR: 0.67, 95% CI: 0.48 to 0.93, P = 0.017), zipcode’s deprivation index being above the US median (OR: 1.26, 95% CI: 1.04 to 1.53, P = 0.018), being diagnosed with CHF before MM (OR: 2.06, 95% CI: 1.56 to 2.72, P < 1.e-3), being male (OR: 1.22, 95% CI: 1.02 to 1.46, P = 0.031), and being over the age of 65 (OR: 1.65, 95% CI: 1.36 to 2, P < 1.e-3).

Conclusion

These findings reveal a previously unknown epistatic interaction between an individual’s predominant genetic ancestry and hypertension, responsible for the CDC-reported higher risk of the African-American population for MM. In other words, hypertension serves as a surrogate marker for a genetic predisposition in individuals with a predominant African genetic ancestry. This insight could improve the screening and identification of minority individuals at risk for MM.
Anomaly detection for high-content image-based phenotypic cell profiling
Authors: Shpigler Alon , Kolet Naor , Golan Shahar , Weisbart Erin , Zaritsky Assaf
Published: June 2024

High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cell’s physiological state. Classical representations of the phenotypic profile can not capture the full underlying complexity in cell organization, while recent weakly machine-learning based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and use it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated for downstream tasks with respect to two classical representations across four public Cell Painting datasets. Anomaly-based representations improved reproducibility, Mechanism of Action classification, and complemented classical representations. Unsupervised explainability of autoencoder-based anomalies identified specific inter-feature dependencies causing anomalies. The general concept of anomaly-based representations can be adapted to other applications in cell biology.
In vivoElectroChromic Shift measurements of photosynthetic activity in far-red absorbing cyanobacteria
Authors: Sellés Julien , Alric Jean , Rutherford A. William , Davis Geoffry A. , Viola Stefania
Published: June 2024

Some cyanobacteria can do photosynthesis using not only visible but also far-red light that is unused by most other oxygenic photoautotrophs because of its lower energy content. These species have a modified photosynthetic apparatus containing red-shifted pigments. The incorporation of red-shifted pigments decreases the photochemical efficiency of photosystem I and, especially, photosystem II, and it might affect the distribution of excitation energy between the two photosystems with possible consequences on the activity of the entire electron transport chain. To investigate thein vivoeffects on photosynthetic activity of these pigment changes, we present here the adaptation of a spectroscopic method, based on a physical phenomenon called ElectroChromic Shift (ECS), to the far-red absorbing cyanobacteriaAcaryochloris marinaandChroococcidiopsis thermalisPCC7203. ECS measures the electric field component of the trans-thylakoid proton motive force generated by photosynthetic electron transfer. We show that ECS can be used in these cyanobacteria to investigatein vivothe stoichiometry of photosystem I and photosystem II and their absorption cross-section, as well as the overall efficiency of light energy conversion into electron transport. Our results indicate that both species use visible and far-red light with similar efficiency, despite significant differences in their light absorption characteristics. ECS thus represents a new non-invasive tool to study the performance of naturally occurring far-red photosynthesis.
Deciphering GB1’s Single Mutational Landscape: Insights from MuMi Analysis
Authors: Guclu Tandac F. , Atilgan Ali Rana , Atilgan Canan
Published: June 2024

Mutational changes that affect the binding of the C2 fragment of Streptococcal protein G (GB1) to the Fc domain of human IgG (IgG-Fc) have been extensively studied using deep mutational scanning (DMS), and the binding affinity of all single mutations has been measured experimentally in the literature. To investigate the underlying molecular basis, we performin-silicomutational scanning for all possible single mutations, along with 2-µs-long molecular dynamics (WT-MD) of the wild-type (WT) GB1 in both unbound and IgG-Fc bound forms. We compute the hydrogen bonds between GB1 and IgG-Fc in WT-MD to identify the dominant hydrogen bonds for binding, which we then assess in conformations produced by Mutation and Minimization (MuMi) to explain the fitness landscape of GB1 and IgG-Fc binding. Furthermore, we analyze MuMi and WT-MD to investigate the dynamics of binding, focusing on the relative solvent accessibility (RSA) of residues and the probability of residues being located at the binding interface. With these analyses, we explain the interactions between GB1 and IgG-Fc and display the structural features of binding. Our findings pave the way for improved predictive accuracy in protein stability and interaction studies, which are crucial for advancements in drug design and synthetic biology.
Comparing statistical learning methods for complex trait prediction from gene expression
Authors: Arango Noah Klimkowski , Morgante Fabio
Published: June 2024

Accurate prediction of complex traits is an important task in quantitative genetics that has become increasingly relevant for personalized medicine. Genotypes have traditionally been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regressions, dimension reductions, and machine learning methods. Recent studies have shown that gene expression levels can produce higher prediction accuracy than genotypes. However, only a few prediction methods were used in these studies. Thus, a comprehensive assessment of methods is needed to fully evaluate the potential of gene expression as a predictor of complex trait phenotypes. Here, we used data from theDrosophilaGenetic Reference Panel (DGRP) to compare the ability of several existing statistical learning methods to predict starvation resistance from gene expression in the two sexes separately. The methods considered differ in assumptions about the distribution of gene effect sizes – ranging from models that assume that every gene affects the trait to more sparse models – and their ability to capture gene-gene interactions. We also used functional annotation (i.e., Gene Ontology (GO)) as an external source of biological information to inform prediction models. The results show that differences in prediction accuracy between methods exist, although they are generally not large. Methods performing variable selection gave higher accuracy in females while methods assuming a more polygenic architecture performed better in males. Incorporating GO annotations further improved prediction accuracy for a few GO terms of biological significance. Biological significance extended to the genes underlying highly predictive GO terms with different genes emerging between sexes. Notably, the Insulin-like Receptor (InR) was prevalent across methods and sexes. Our results confirmed the potential of transcriptomic prediction and highlighted the importance of selecting appropriate methods and strategies in order to achieve accurate predictions.
Genetic Structuring and Conservation of Asian Sockeye Salmon: Identification of Regional Stock Complexes
Authors: Khrustaleva Anastasia M.
Published: June 2024

In order to describe large-scale spatial structure of Asian sockeye salmon the variability of 45 SNP loci was analyzed in 22 samples from the North-West coast of the Pacific Ocean. Three large regional population complexes were identified: southwestern Kamchatka, Kamchatka River basin, and the North-East (comprising stocks from Koryak Highlands). Populations within the identified complexes are connected by gene migration and have a common origin, close geographic proximity, comparable climatic, landscape and environmental conditions in the freshwater and early marine periods of life. Populations confined to watersheds of the North coast of the Sea of Okhotsk (Palana and Okhota rivers), along with island populations, displayed noteworthy distinctions from the isolated population complexes. We hypothesize that the marked divergence observed in island populations is primarily caused by genetic drift occurring during long periods of isolation. The pronounced divergence of Palana River population may be the result of both genetic drift and natural selection, driven by the challenging smoltification and juvenile transition to the ocean, along with local adaptations during spawning and early life periods in the Palansky Lake. At the same time in the Okhota River population, demographic factors such as genetic drift and bottlenecks played a key role.
Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction
Authors: Taguchi Y-h , Turki Turki
Published: June 2024

The evaluation of drug-gene-disease interactions is key for the identification of drugs effective against disease. However, at present, drugs that are effective against genes that are critical for disease are difficult to identify. Following a disease-centric approach, there is a need to identify genes critical to disease function and find drugs that are effective against them. By contrast, following a drug-centric approach comprises identifying the genes targeted by drugs, and then the diseases in which the identified genes are critical. Both of these processes are complex. Using a gene-centric approach, whereby we identify genes that are effective against the disease and can be targeted by drugs, is much easier. However, how such sets of genes can be identified without specifying either the target diseases or drugs is not known. In this study, a novel artificial intelligencebased approach that employs unsupervised methods and identifies genes without specifying neither diseases nor drugs is presented. To evaluate its feasibility, we applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to perform drug repositioning from protein-protein interactions (PPI) without any other information. Proteins selected by TD-based unsupervised FE include many genes related to cancers, as well as drugs that target the selected proteins. Thus, we were able to identify cancer drugs using only PPI. Because the selected proteins had more interactions, we replaced the selected proteins with hub proteins and found that hub proteins themselves could be used for drug repositioning. In contrast to hub proteins, which can only identify cancer drugs, TD-based unsupervised FE enables the identification of drugs for other diseases. In addition, TD-based unsupervised FE can be used to identify drugs that are effective inin vivoexperiments, which is difficult when hub proteins are used. In conclusion, TD-based unsupervised FE is a useful tool for drug repositioning using only PPI without other information.
Spatial Multiomics Lipids and Gene expression using MALDI ISH MSI
Authors: Vanderschoot Kyle A. , Padilla Jacob P. , Steineman Kelli A. , De Caro Christopher M. , Heffern Marie C. , Neumann Elizabeth K.
Published: June 2024

Current spatial gene expression methods use DNA microarrays, Next Generation Sequencing (NGS), and fluorescence microscopy to depict the pathological/histological architecture of tissues. While each of these techniques has its own advantages, they are often costly, time intensive, and limit sampling area. A newly developed mass spectrometry-based platform, MADLI ISH MSI, combinesin-situhybridization (ISH) with matrix-assisted laser desorption/ionization (MALDI) to indirectly detect mRNA through an azide-modified photocleavable peptide mass tag using a single RNA targeting probe sequence. To date, 20 photocleavable mRNA probes have been synthesized to provide cellular identity within full sagittal sections of fresh frozen murine brain. This information can then be combined with existing MALDI techniques to overlay metabolomic data, such as lipids, to connect the functional state of a cell with its expressed genes. Future directions include expanding upon the number of genes that can be targeted within a single experiment.
A systematic review on diagnostic and prognostic biomarkers for bladder cancer
Authors: Muhammad Umar , Ahmad Umar , Ibrahim Buhari , Ahmad Aliyu Adamu , Liman Haruna Usman
Published: June 2024

Background

Bladder cancer is one of the most prevalent malignancies worldwide. Despite its high incidence, public awareness of the condition remains low, and it has received less research attention compared to other common cancers. Over the past 80 years, patient outcomes and treatment strategies have remained largely unchanged, with cystoscopy being the primary method for detecting bladder cancer. This procedure, often repeated during long-term surveillance due to the recurrent nature of bladder tumors, is both uncomfortable for patients and costly for healthcare providers. The identification and validation of molecular biomarkers in blood, urine, or tissue could facilitate tumour detection and reduce reliance on cystoscopy.

Aim

This study aims to identify potential molecular biomarkers for bladder cancer that could improve tumour detection and lessen the need for repeated cystoscopies.

Methods

A systematic review was conducted, searching for articles related to bladder cancer biomarkers in four databases: PubMed, ScienceDirect, Google Scholar, and Cochrane. Studies that met the inclusion criteria underwent title/abstract screening and full-text review. A total of twenty studies were deemed eligible for inclusion in this review.

Results

The review identified several gene product biomarkers, including TEAD4, TPM1, TPM2, SKA3, EO1, HYAL3, MTDH, EPDR1, hTERT, KRT7, SW, ARHGAP9, XPH4, OTX1, BUB1, and Usp28. Additionally, protein product biomarkers were identified, such as A1AT, APOE, AG, CA9, IL8, MMP9, MMP10, PAI1, SCDI1, SDC1, VEGFA, CD73, TIP2, CXCL5, PCAT6, and NCR3LG1 (B7-H6).

Conclusion

The study highlights the potential of various gene and protein biomarkers for the detection of bladder cancer. Further research is necessary to validate these biomarkers’ diagnostic and prognostic potential in identifying bladder cancer in suspected cases.
Mathematical and Statistical Approaches in Epidemiological Investigation of Hospital Infection: A Case Study of the 2015 Middle East Respiratory Syndrome Outbreak in Korea
Authors: Ko Youngsuk , Jung Eunok
Published: June 2024

Summary Introduction

Epidemiological investigations are used to identify outbreaks, collect data, and implement control measures, playing a crucial role in disease control and prevention. Mathematical and statistical approaches enhance these investigations by incorporating data analysis to understand disease characteristics and provide insights.

Methods

To estimate the transmissibility of a disease, we introduce a process to derive the likelihood function using individual patient information from epidemiological investigation. This method was applied to the case of Pyeongtaek St. Mary’s Hospital during the 2015 Middle East Respiratory Syndrome outbreak in Korea. A stochastic model was developed, and scenario analysis reflecting actual outbreak progress, risk factors, and mask mandates was conducted.

Results

We applied transmission rate estimation to the Pyeongtaek St. Mary’s Hospital case, showing a high patient-to-patient transmission rate. The superspreader was observed to have approximately 25 times higher transmissibility than other patients. Given these conditions, if hospital transmission period is prolonged, number of cases could be three times higher than the actual incidence. The effect of mask-wearing in hospital was investigated based on the type of mask and the intensity of the intervention. It was found that the scale of epidemic could be reduced by a maximum of 77% and a minimum of 17%.

Conclusions

Through the application of mathematical and statistical methodologies in epidemiological investigations, this study identified and quantified risk factors. Methodology of this study can be easily adapted and applied to other diseases and is expected to help in establishing effective strategies to fight against emerging infectious diseases.