A Critical Review of the Impact of Candidate Copy Number Variants on Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder (NDD) influenced by genetic, epigenetic, and environmental factors. Recent advancements in genomic analysis have shed light on numerous genes associated with ASD, highlighting the significant role of both common and rare genetic mutations, as well as copy number variations (CNVs), single nucleotide polymorphisms (SNPs) and unique de novo variants. These genetic variations disrupt neurodevelopmental pathways, contributing to the disorder's complexity. Notably, CNVs are present in 10%-20% of individuals with autism, with 3%-7% detectable through cytogenetic methods. While the role of submicroscopic CNVs in ASD has been recently studied, their association with genomic loci and genes has not been thoroughly explored. In this review, we focus on 47 CNV regions linked to ASD, encompassing 1,632 genes, including protein-coding genes and long non-coding RNAs (lncRNAs), of which 659 show significant brain expression. Using a list of ASD-associated genes from SFARI, we detect 17 regions harboring at least one known ASD-related protein-coding gene. Of the remaining 30 regions, we identify 24 regions containing at least one protein-coding gene with brain-enriched expression and a nervous system phenotype in mouse mutants, and one lncRNA with both brain-enriched expression and upregulation in iPSC to neuron differentiation. This review not only expands our understanding of the genetic diversity associated with ASD but also underscores the potential of lncRNAs in contributing to its etiology. Additionally, the discovered CNVs will be a valuable resource for future diagnostic, therapeutic, and research endeavors aimed at prioritizing genetic variations in ASD.

Insights into the production and evolution of lantibiotics from a computational analysis of peptides associated with the lanthipeptide cyclase domain.

Lanthipeptides are a large group of ribosomally encoded peptides cyclized by thioether and methylene bridges, which include the lantibiotics, lanthipeptides with antimicrobial activity. There are over 100 experimentally characterized lanthipeptides, with at least 25 distinct cyclization bridging patterns. We set out to understand the evolutionary dynamics and diversity of lanthipeptides. We identified 977 peptides in 2785 bacterial genomes from short open-reading frames encoding lanthipeptide modifiable amino acids (C, S and T) that lay chromosomally adjacent to genes encoding proteins containing the cyclase domain. These appeared to be synthesized by both known and novel enzymatic combinations. Our predictor of bridging topology suggested 36 novel-predicted topologies, including a single-cysteine topology seen in 179 lanthionine or labionin containing peptides, which were enriched for histidine. Evidence that supported the relevance of the single-cysteine containing lanthipeptide precursors included the presence of the labionin motif among single cysteine peptides that clustered with labionin-associated synthetase domains, and the leader features of experimentally defined lanthipeptides that were shared with single cysteine predictions. Evolutionary rate variation among peptide subfamilies suggests that selection pressures for functional change differ among subfamilies. Lanthipeptides that have recently evolved specific novel features may represent a richer source of potential novel antimicrobials, since their target species may have had less time to evolve resistance.

Future-proofing genomic data and consent management: a comprehensive review of technology innovations.

Genomic information is increasingly used to inform medical treatments and manage future disease risks. However, any personal and societal gains must be carefully balanced against the risk to individuals contributing their genomic data. Expanding our understanding of actionable genomic insights requires researchers to access large global datasets to capture the complexity of genomic contribution to diseases. Similarly, clinicians need efficient access to a patient's genome as well as population-representative historical records for evidence-based decisions. Both researchers and clinicians hence rely on participants to consent to the use of their genomic data, which in turn requires trust in the professional and ethical handling of this information. Here, we review existing and emerging solutions for secure and effective genomic information management, including storage, encryption, consent, and authorization that are needed to build participant trust. We discuss recent innovations in cloud computing, quantum-computing-proof encryption, and self-sovereign identity. These innovations can augment key developments from within the genomics community, notably GA4GH Passports and the Crypt4GH file container standard. We also explore how decentralized storage as well as the digital consenting process can offer culturally acceptable processes to encourage data contributions from ethnic minorities. We conclude that the individual and their right for self-determination needs to be put at the center of any genomics framework, because only on an individual level can the received benefits be accurately balanced against the risk of exposing private information.

Optimization of a mouse model of pancreatic cancer to simulate the human phenotypes of metastasis and cachexia.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) presents with a high mortality rate. Two important features of PDAC contribute to this poor outcome. The first is metastasis which occurs in ~ 80% of PDAC patients. The second is cachexia, which compromises treatment tolerance for patients and reduces their quality of life. Although various mouse models of PDAC exist, recapitulating both metastatic and cachectic features have been challenging. METHODS: Here, we optimize an orthotopic mouse model of PDAC by altering several conditions, including the subcloning of parental murine PDAC cells, implantation site, number of transplanted cells, and age of recipient mice. We perform spatial profiling to compare primary and metastatic immune microenvironments and RNA sequencing to gain insight into the mechanisms of muscle wasting in PDAC-induced cachexia, comparing non-metastatic to metastatic conditions. RESULTS: These modifications extend the time course of the disease and concurrently increase the rate of metastasis to approximately 70%. Furthermore, reliable cachexia endpoints are achieved in both PDAC mice with and without metastases, which is reminiscent of patients. We also find that cachectic muscles from PDAC mice with metastasis exhibit a similar transcriptional profile to muscles derived from mice and patients without metastasis. CONCLUSION: Together, this model is likely to be advantageous in both advancing our understanding of the mechanism of PDAC cachexia, as well as in the evaluation of novel therapeutics.

Reversibility in the Physical Properties of Agarose Gels following an Exchange in Solvent and Non-Solvent.

Agarose forms a homogeneous thermoreversible gel in an aqueous solvent above a critical polymer concentration. Contrary to the prevailing consensus, recent confirmations indicate that agarose gels are also stable in non-solvents like acetone and ethanol. A previous study compared gel characterisations and behaviours in water and ethanol, discussing the gelation mechanism. In the current work, the ethanol gel is exchanged with water to explore the potential reversibility of the displacement of water in agarose. Initially, the structure is characterised using 1H NMR in DMSO-d6 and D2O solvents. Subsequently, a very low yield (0.04) of methyl substitution per agarobiose unit is determined. The different gels after stabilisation are characterised using rheology, and their physical properties are compared based on the solvent used. The bound water molecules, acting as plasticizers in aqueous medium, are likely removed during the exchange process with ethanol, resulting in a stronger and more fragile gel. Next, the gel obtained after the second exchange from ethanol back to water is compared with the initial gel prepared in water. This is the first time where such gel has been characterised without undergoing a phase transition when switching from a good solvent to a non-solvent, and vice versa, thereby testing the reversibility of the solvent exchange. Reversibility of this behaviour is demonstrated through swelling and rheology experiments. This study extends the application of agarose in chromatography and electrophoresis.

Comprehensive experimental dataset on large-amplitude Rayleigh-Plateau instability in continuous InkJet printing regime.

The Rayleigh-Plateau instability, a phenomenon of paramount significance in fluid dynamics, finds widespread application in the Continuous InkJet (CIJ) printing process. This study presents a comprehensive dataset comprising experimental investigations of fluid jet breakup phenomena under large-amplitude stimulation conditions using an industrial CIJ print-head from Markem-Imaje. Unlike previous studies, this dataset encompasses a diverse range of experimental conditions, including nine different Newtonian fluids with meticulously measured rheological properties (viscosities, surface tensions and densities). The applied stimulation amplitudes vary from 5V to 45V, representing a substantial span of excitation levels. The experimental setup captures the intricate dynamics of fluid jets subjected to these varying conditions, producing a rich collection of over 5,000 high-resolution images depicting the breakup phenomena. Each amplitude of stimulation and fluid type yields more than 55 distinct images, providing detailed insights into the evolving jet morphologies. To ensure the accuracy and relevance of the dataset, all ejection parameters are rigorously documented and included. The dataset thus serves as a valuable resource for researchers seeking to explore the dynamics of large-amplitude Rayleigh-Plateau instability in CIJ printing. Its comprehensiveness and diversity make it particularly suitable for the application of novel machine learning and deep-learning approaches, enabling the study of jet morphological evolution beyond the confines of classical Rayleigh's theory. This dataset holds promise for advancing our understanding of fluid jet dynamics and enhancing the efficiency and quality of CIJ printing processes.

Data Visualization of CRISPR-Cas9 Guide RNA Design Tools.

With the advancement of genomic engineering and genetic modification techniques, the uptake of computational tools to design guide RNA increased drastically. Searching for genomic targets to design guides with maximum on-target activity (efficiency) and minimum off-target activity (specificity) is now an essential part of genome editing experiments. Today, a variety of tools exist that allow the search of genomic targets and let users customize their search parameters to better suit their experiments. Here we present an overview of different ways to visualize these searched CRISPR target sites along with specific downstream information like primer design, restriction enzyme activity and mutational outcome prediction after a double-stranded break. We discuss the importance of a good visualization summary to interpret information along with different ways to represent similar information effectively.

PEPS: Polygenic Epistatic Phenotype Simulation.

Genetic data is limited and generating new datasets is often an expensive, time-consuming process, involving countless moving parts to genotype and phenotype individuals. While sharing data is beneficial for quality control and software development, privacy and security are of utmost importance. Generating synthetic data is a practical solution to mitigate the cost, time and sensitivities that hamper developers and researchers in producing and validating novel biotechnological solutions to data intensive problems. Existing methods focus on mutation frequencies at specific loci while ignoring epistatic interactions. Alternatively, programs that do consider epistasis are limited to two-way interactions or apply genomic constraints that make synthetic data generation arduous or computationally intensive. To solve this, we developed Polygenic Epistatic Phenotype Simulator (PEPS). Our tool is a probabilistic model that can generate synthetic phenotypes with a controllable level of complexity.

An Approach for Generating Realistic Australian Synthetic Healthcare Data.

Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient's anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics.

Prediction of Coronary Artery Disease Risk Using Genetic and Phenotypic Variables.

Coronary artery disease (CAD) has the highest disease burden worldwide. To manage this burden, predictive models are required to screen patients for preventative treatment. A range of variables have been explored for their capacity to predict disease, including phenotypic (age, sex, BMI and smoking status), medical imaging (carotid artery thickness) and genotypic. We use a machine learning models and the UK Biobank cohort to measure the prediction capacity of these 3 variable categories, both in combination and isolation. We demonstrate that phenotypic variables from the Framingham risk score have the best prediction capacity, although a combination of phenotypic, medical imaging and genotypic variables deliver the most specific models. Furthermore, we demonstrate that Variant Spark, a random forest based GWAS platform, performs effective feature selection for SNP-based genotype variables, identifying 115 significantly associated SNPs to the CAD phenotype.

The impact of inflammation and acute phase activation in cancer cachexia.

The development of cachexia in the setting of cancer or other chronic diseases is a significant detriment for patients. Cachexia is associated with a decreased ability to tolerate therapies, reduction in ambulation, reduced quality of life, and increased mortality. Cachexia appears intricately linked to the activation of the acute phase response and is a drain on metabolic resources. Work has begun to focus on the important inflammatory factors associated with the acute phase response and their role in the immune activation of cachexia. Furthermore, data supporting the liver, lung, skeletal muscle, and tumor as all playing a role in activation of the acute phase are emerging. Although the acute phase is increasingly being recognized as being involved in cachexia, work in understanding underlying mechanisms of cachexia associated with the acute phase response remains an active area of investigation and still lack a holistic understanding and a clear causal link. Studies to date are largely correlative in nature, nonetheless suggesting the possibility for a role for various acute phase reactants. Herein, we examine the current literature regarding the acute phase response proteins, the evidence these proteins play in the promotion and exacerbation of cachexia, and current evidence of a therapeutic potential for patients.

Cell-Penetrating Milk-Derived Peptides with a Non-Inflammatory Profile.

Milk-derived peptides are known to confer anti-inflammatory effects. We hypothesised that milk-derived cell-penetrating peptides might modulate inflammation in useful ways. Using computational techniques, we identified and synthesised peptides from the milk protein Alpha-S1-casein that were predicted to be cell-penetrating using a machine learning predictor. We modified the interpretation of the prediction results to consider the effects of histidine. Peptides were then selected for testing to determine their cell penetrability and anti-inflammatory effects using HeLa cells and J774.2 mouse macrophage cell lines. The selected peptides all showed cell penetrating behaviour, as judged using confocal microscopy of fluorescently labelled peptides. None of the peptides had an effect on either the NF-κB transcription factor or TNFα and IL-1ß secretion. Thus, the identified milk-derived sequences have the ability to be internalised into the cell without affecting cell homeostatic mechanisms such as NF-κB activation. These peptides are worthy of further investigation for other potential bioactivities or as a naturally derived carrier to promote the cellular internalisation of other active peptides.

Predicting CRISPR-Cas12a guide efficiency for targeting using machine learning.

Genome editing through the development of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat)-Cas technology has revolutionized many fields in biology. Beyond Cas9 nucleases, Cas12a (formerly Cpf1) has emerged as a promising alternative to Cas9 for editing AT-rich genomes. Despite the promises, guide RNA efficiency prediction through computational tools search still lacks accuracy. Through a computational meta-analysis, here we report that Cas12a target and off-target cleavage behavior are a factor of nucleotide bias combined with nucleotide mismatches relative to the protospacer adjacent motif (PAM) site. These features helped to train a Random Forest machine learning model to improve the accuracy by at least 15% over existing algorithms to predict guide RNA efficiency for the Cas12a enzyme. Despite the progresses, our report underscores the need for more representative datasets and further benchmarking to reliably and accurately predict guide RNA efficiency and off-target effects for Cas12a enzymes.

Novel Alzheimer's disease genes and epistasis identified using machine learning GWAS platform.

Alzheimer's disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this 'missing heritability', however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci, APOE, and identify two novel genome-wide significant AD associated loci in both cohorts, SH3BP4 and SASH1, which are also in significant epistatic interactions with APOE. We show that the SH3BP4 SNP has a modulating effect on the known pathogenic APOE SNP, demonstrating a possible protective mechanism against AD. SASH1 is involved in a triplet interaction with pathogenic APOE SNP and ACOT11, where the SASH1 SNP lowered the pathogenic interaction effect between ACOT11 and APOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.

Thresholding Gini variable importance with a single-trained random forest: An empirical Bayes approach.

Random forests (RFs) are a widely used modelling tool capable of feature selection via a variable importance measure (VIM), however, a threshold is needed to control for false positives. In the absence of a good understanding of the characteristics of VIMs, many current approaches attempt to select features associated to the response by training multiple RFs to generate statistical power via a permutation null, by employing recursive feature elimination, or through a combination of both. However, for high-dimensional datasets these approaches become computationally infeasible. In this paper, we present RFlocalfdr, a statistical approach, built on the empirical Bayes argument of Efron, for thresholding mean decrease in impurity (MDI) importances. It identifies features significantly associated with the response while controlling the false positive rate. Using synthetic data and real-world data in health, we demonstrate that RFlocalfdr has equivalent accuracy to currently published approaches, while being orders of magnitude faster. We show that RFlocalfdr can successfully threshold a dataset of 106 datapoints, establishing its usability for large-scale datasets, like genomics. Furthermore, RFlocalfdr is compatible with any RF implementation that returns a VIM and counts, making it a versatile feature selection tool that reduces false discoveries.

Comparison Adsorption of Cd (II) onto Lignin and Polysaccharide-Based Polymers.

Given the predominantly negative impact of heavy metals on living organisms, the present study proposed to evaluate the adsorption performances under static conditions of Cd (II) from aqueous solutions on unmodified Sarkanda grass lignin compared to the adsorption performances of polysaccharide polymers chemically functionalized, obtained by synthesis and in their native state, but which, although effective, have a cost price that does not allow for large-scale expansion. To improve the retention of Cd (II) on this aromatic component of the biomass resulting from the processing of lignocellulosic materials, different experimental conditions (pH, concentration, dose and contact time) were followed. The Freundlich and Langmuir isotherms were used to describe the equilibrium conditions. Adsorption kinetics were assessed using the Lagergren I and Ho and McKay II kinetic models, furnishing informative insights into the process mechanism. Lignin adsorption capacity was also analyzed by performing biological tests on tomato seeds (Lypercosium esculentum), since heavy metals are known to be a stress factor for seeds by disturbing the osmotic equilibrium. Through the prism of the investigated parameters and under precisely established experimental conditions, unmodified Sarkanda grass lignin-an aromatic biopolymer-can be recommended as a promising adsorbent for the retention of Cd (II) from aqueous solutions, successfully replacing polysaccharide, especially cellulose-based polymers.

The multifaceted aspects of sleep and sleep-wake disorders following stroke.

Sleep-wake disorders (SWD) are acknowledged risk factors for both ischemic stroke and poor cardiovascular and functional outcome after stroke. SWD are frequent following stroke, with sleep apnea (SA) being the most frequent SWD affecting more than half of stroke survivors. While sleep disturbances and SWD are frequently reported in the acute phase, they may persist in the chronic phase after an ischemic stroke. Despite the frequency and risk associated with SWD following stroke, screening for SWD remains rare in the clinical setting, due to challenges in the assessment of post-stroke SWD, uncertainty regarding the optimal timing for their diagnosis, and a lack of clear treatment guidelines (i.e., when to treat and the optimal treatment strategy). However, little evidence support the feasibility of SWD treatment even in the acute phase of stroke and its favorable effect on long-term cardiovascular and functional outcomes. Thus, sleep health recommendations and SWD treatment should be systematically embedded in secondary stroke prevention strategy. We therefore propose that the management of SWD associated with stroke should rely on a multidisciplinary approach, with an integrated diagnostic, treatment, and follow-up strategy. The challenges in the field are to improve post-stroke SWD diagnosis, prognosis and treatment, through a better appraisal of their pathophysiology and temporal evolution.

Different time course recovery of muscle edema within the quadriceps femoris and functional performance after single- vs multi-joint exercises.

This study aimed to verify the time course recovery of muscle edema within the quadriceps femoris and functional performance after lower-body single- and multi-joint exercises. For this within-participant unilateral and contralateral experimental design, fourteen untrained young males performed a unilateral knee extension exercise (KE), and a unilateral leg press (LP) exercise in a counterbalanced order. At pre-, post-, 24 h, 48 h, 72 h, and 96 h after exercise, the peak torque (PT), unilateral countermovement jump (uCMJ) performance, and rectus femoris (RF) and vastus lateralis (VL) muscle thicknesses were recorded in both legs. The PT decreased immediately after (p = 0.01) both exercises (KE and LP) and was fully recovered 24 h after KE (p = 0.38) and 48 h after LP (p = 0.68). Jump height and power, in the uCMJ, followed the same PT recovery pattern after both exercises. However, vertical stiffness (Kvert) was not affected at any time point after both protocols. The RF thickness increased after both exercises (p = 0.01) and was fully restored 48 h after KE (p = 0.86) and 96 h after LP (p = 1.00). The VL thickness increased after both exercises (p = 0.01) and was fully restored 24 h after LP (p = 1.00) and 48 h after KE (p = 1.00). The LP exercise, compared to KE, induced more prolonged impairment of functional performance and delayed recovery of RF muscle edema. However, the VL edema-induced muscle swelling recovery was delayed after the KE exercise. The different recovery kinetics between functional performance and muscle damage should be taken into consideration depending on the objectives of the next training sessions.

Characterization of Agarose Gels in Solvent and Non-Solvent Media.

Agarose is known to form a homogeneous thermoreversible gel in an aqueous medium over a critical polymer concentration. The solid-liquid phase transitions are thermoreversible but depend on the molecular structure of the agarose sample tested. The literature has mentioned that agarose gels could remain stable in non-solvents such as acetone or ethanol. However, there has been no characterization of their behavior nor a comparison with the gels formed in a good solvent such as water. In the first step of this article, the structure was characterized using 1H and 13C NMR in both D2O and DMSO-d6 solvents. DMSO is a solvent that dissolves agarose regardless of the temperature. First, we have determined a low yield of methyl substitution on the D-galactose unit. Then, the evolution of the 1H NMR spectrum was monitored as a function of temperature during both increasing and decreasing temperature processes, ranging from 25 to 80 °C. A large thermal hysteresis was obtained and discussed, which aided in the interpretation of rheological behavior. The hysteresis of NMR signals is related to the mobility of the agarose chains, which follows the sol/gel transition depending on the chains' association with H-bonds between water and the -OH groups of agarose for tightly bound water and agarose/agarose in chain packing. In the second step of the study, the water in the agarose gel was exchanged with ethanol, which is a non-solvent for agarose. The resulting gel was stable, and its properties were characterized using rheology and compared to its behavior in aqueous media. The bound water molecules that act as plasticizers were likely removed during the exchange process, resulting in a stronger and more brittle gel in ethanol, with higher thermal stability compared to the aqueous gel. It is the first time that such gel is characterized without phase transition when passing from a good solvent to a non-solvent. This extends the domains of application of agarose.