VarChat: the generative AI assistant for the interpretation of human genomic variations.

MOTIVATION: In the modern era of genomic research, the scientific community is witnessing an explosive growth in the volume of published findings. While this abundance of data offers invaluable insights, it also places a pressing responsibility on genetic professionals and researchers to stay informed about the latest findings and their clinical significance. Genomic variant interpretation is currently facing a challenge in identifying the most up-to-date and relevant scientific papers, while also extracting meaningful information to accelerate the process from clinical assessment to reporting. Computer-aided literature search and summarization can play a pivotal role in this context. By synthesizing complex genomic findings into concise, interpretable summaries, this approach facilitates the translation of extensive genomic datasets into clinically relevant insights. RESULTS: To bridge this gap, we present VarChat (varchat.engenome.com), an innovative tool based on generative AI, developed to find and summarize the fragmented scientific literature associated with genomic variants into brief yet informative texts. VarChat provides users with a concise description of specific genetic variants, detailing their impact on related proteins and possible effects on human health. In addition, VarChat offers direct links to related scientific trustable sources, and encourages deeper research. AVAILABILITY AND IMPLEMENTATION: varchat.engenome.com.

RNA expression profiling in lymphoblastoid cell lines from mutated and non-mutated amyotrophic lateral sclerosis patients.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the death of upper and lower motor neurons with an unknown etiology. The difficulty of recovering biological material from patients led to employ lymphoblastoid cell lines (LCLs) as a model for ALS because many pathways, typically located in neurons, are also activated in these cells. METHODS: To investigate the expression of coding and long non-coding RNAs in LCLs, a transcriptomic profiling of sporadic ALS (SALS) and mutated patients (FUS, TARDBP, C9ORF72 and SOD1) and matched controls was realized. Thus, differentially expressed genes (DEGs) were investigated among the different subgroups of patients. Peripheral blood mononuclear cells (PBMCs) were isolated and immortalized into LCLs via Epstein-Barr virus infection; RNA was extracted, and RNA-sequencing analysis was performed. RESULTS: Gene expression profiles of LCLs were genetic-background-specific; indeed, only 12 genes were commonly deregulated in all groups. Nonetheless, pathways enriched by DEGs in each group were also compared, and a total of 89 Kyoto Encyclopedia of Genes and Genomes (KEGG) terms were shared among all patients. Eventually, the similarity of affected pathways was also assessed when our data were matched with a transcriptomic profile realized in the PBMCs of the same patients. CONCLUSIONS: We conclude that LCLs are a good model for the study of RNA deregulation in ALS.

MINCR: A long non-coding RNA shared between cancer and neurodegeneration.

The multitasking nature of lncRNAs allows them to play a central role in both physiological and pathological conditions. Often the same lncRNA can participate in different diseases. Specifically, the MYC-induced Long non-Coding RNA MINCR is upregulated in various cancer types, while downregulated in Amyotrophic Lateral Sclerosis patients. Therefore, this work aims to investigate MINCR potential mechanisms of action and its implications in cancer and neurodegeneration in relation to its expression levels in SH-SY5Y cells through RNA-sequencing approach. Our results show that MINCR overexpression causes massive alterations in cancer-related genes, leading to disruption in many fundamental processes, such as cell cycle and growth factor signaling. On the contrary, MINCR downregulation influences a small number of genes involved in different neurodegenerative disorders, mostly concerning RNA metabolism and inflammation. Thus, understanding the cause and functional consequences of MINCR deregulation gives important insights on potential pathogenetic mechanisms both in cancer and in neurodegeneration.

ViR: a tool to solve intrasample variability in the prediction of viral integration sites using whole genome sequencing data.

BACKGROUND: Several bioinformatics pipelines have been developed to detect sequences from viruses that integrate into the human genome because of the health relevance of these integrations, such as in the persistence of viral infection and/or in generating genotoxic effects, often progressing into cancer. Recent genomics and metagenomics analyses have shown that viruses also integrate into the genome of non-model organisms (i.e., arthropods, fish, plants, vertebrates). However, rarely studies of endogenous viral elements (EVEs) in non-model organisms have gone beyond their characterization from reference genome assemblies. In non-model organisms, we lack a thorough understanding of the widespread occurrence of EVEs and their biological relevance, apart from sporadic cases which nevertheless point to significant roles of EVEs in immunity and regulation of expression. The concomitance of repetitive DNA, duplications and/or assembly fragmentations in a genome sequence and intrasample variability in whole-genome sequencing (WGS) data could determine misalignments when mapping data to a genome assembly. This phenomenon hinders our ability to properly identify integration sites. RESULTS: To fill this gap, we developed ViR, a pipeline which solves the dispersion of reads due to intrasample variability in sequencing data from both single and pooled DNA samples thus ameliorating the detection of integration sites. We tested ViR to work with both in silico and real sequencing data from a non-model organism, the arboviral vector Aedes albopictus. Potential viral integrations predicted by ViR were molecularly validated supporting the accuracy of ViR results. CONCLUSION: ViR will open new venues to explore the biology of EVEs, especially in non-model organisms. Importantly, while we generated ViR with the identification of EVEs in mind, its application can be extended to detect any lateral transfer event providing an ad-hoc sequence to interrogate.

Different miRNA Profiles in Plasma Derived Small and Large Extracellular Vesicles from Patients with Neurodegenerative Diseases.

Identifying biomarkers is essential for early diagnosis of neurodegenerative diseases (NDs). Large (LEVs) and small extracellular vesicles (SEVs) are extracellular vesicles (EVs) of different sizes and biological functions transported in blood and they may be valid biomarkers for NDs. The aim of our study was to investigate common and different miRNA signatures in plasma derived LEVs and SEVs of Alzheimer's disease (AD), Parkinson's disease (PD), Amyotrophic Lateral Sclerosis (ALS) and Fronto-Temporal Dementia (FTD) patients. LEVs and SEVs were isolated from plasma of patients and healthy volunteers (CTR) by filtration and differential centrifugation and RNA was extracted. Small RNAs libraries were carried out by Next Generation Sequencing (NGS). MiRNAs discriminate all NDs diseases from CTRs and they can provide a signature for each NDs. Common enriched pathways for SEVs were instead linked to ubiquitin mediated proteolysis and Toll-like receptor signaling pathways and for LEVs to neurotrophin signaling and Glycosphingolipid biosynthesis pathway. LEVs and SEVs are involved in different pathways and this might give a specificity to their role in the spreading of the disease. The study of common and different miRNAs transported by LEVs and SEVs can be of great interest for biomarker discovery and for pathogenesis studies in neurodegeneration.

Alzheimer's, Parkinson's Disease and Amyotrophic Lateral Sclerosis Gene Expression Patterns Divergence Reveals Different Grade of RNA Metabolism Involvement.

Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) are neurodegenerative disorders characterized by a progressive degeneration of the central or peripheral nervous systems. A central role of the RNA metabolism has emerged in these diseases, concerning mRNAs processing and non-coding RNAs biogenesis. We aimed to identify possible common grounds or differences in the dysregulated pathways of AD, PD, and ALS. To do so, we performed RNA-seq analysis to investigate the deregulation of both coding and long non-coding RNAs (lncRNAs) in ALS, AD, and PD patients and controls (CTRL) in peripheral blood mononuclear cells (PBMCs). A total of 293 differentially expressed (DE) lncRNAs and 87 mRNAs were found in ALS patients. In AD patients a total of 23 DE genes emerged, 19 protein coding genes and four lncRNAs. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses, we found common affected pathways and biological processes in ALS and AD. In PD patients only five genes were found to be DE. Our data brought to light the importance of lncRNAs and mRNAs regulation in three principal neurodegenerative disorders, offering starting points for new investigations on deregulated pathogenic mechanisms.

Fermentation of lactose to ethanol in cheese whey permeate and concentrated permeate by engineered Escherichia coli.

BACKGROUND: Whey permeate is a lactose-rich effluent remaining after protein extraction from milk-resulting cheese whey, an abundant dairy waste. The lactose to ethanol fermentation can complete whey valorization chain by decreasing dairy waste polluting potential, due to its nutritional load, and producing a biofuel from renewable source at the same time. Wild type and engineered microorganisms have been proposed as fermentation biocatalysts. However, they present different drawbacks (e.g., nutritional supplements requirement, high transcriptional demand of recombinant genes, precise oxygen level, and substrate inhibition) which limit the industrial attractiveness of such conversion process. In this work, we aim to engineer a new bacterial biocatalyst, specific for dairy waste fermentation. RESULTS: We metabolically engineered eight Escherichia coli strains via a new expression plasmid with the pyruvate-to-ethanol conversion genes, and we carried out the selection of the best strain among the candidates, in terms of growth in permeate, lactose consumption and ethanol formation. We finally showed that the selected engineered microbe (W strain) is able to efficiently ferment permeate and concentrated permeate, without nutritional supplements, in pH-controlled bioreactor. In the conditions tested in this work, the selected biocatalyst could complete the fermentation of permeate and concentrated permeate in about 50 and 85 h on average, producing up to 17 and 40 g/l of ethanol, respectively. CONCLUSIONS: To our knowledge, this is the first report showing efficient ethanol production from the lactose contained in whey permeate with engineered E. coli. The selected strain is amenable to further metabolic optimization and represents an advance towards efficient biofuel production from industrial waste stream.

Analysis of amplicon-based NGS data from neurological disease gene panels: a new method for allele drop-out management.

BACKGROUND: Amplicon-based targeted resequencing is a commonly adopted solution for next-generation sequencing applications focused on specific genomic regions. The reliability of such approaches rests on the high specificity and deep coverage, although sequencing artifacts attributable to PCR-like amplification can be encountered. Between these artifacts, allele drop-out, which is the preferential amplification of one allele, causes an artificial increase in homozygosity when heterozygous mutations fall on a primer pairing region. Here, a procedure to manage such artifacts, based on a pipeline composed of two steps of alignment and variant calling, is proposed. This methodology has been compared to the Illumina Custom Amplicon workflow, available on Illumina MiSeq, on the analysis of data obtained with four newly designed TruSeq Custom Amplicon gene panels. RESULTS: Four gene panels, specific for Parkinson disease, for Intracerebral Hemorrhage Diseases (COL4A1 and COL4A2 genes) and for Familial Hemiplegic Migraine (CACNA1A and ATP1A2 genes) were designed. A total of 119 samples were re-sequenced with Illumina MiSeq sequencer and panel characterization in terms of coverage, number of variants found and allele drop-out potential impact has been carried out. Results show that 14 % of identified variants is potentially affected by allele drop-out artifacts and that both the Custom Amplicon workflow and the procedure proposed here could correctly identify them. Furthermore, a more complex configuration in presence of two mutations was simulated in silico. In this configuration, our proposed methodology outperforms Custom Amplicon workflow, being able to correctly identify two mutations in all the studied configurations. CONCLUSIONS: Allele drop-out plays a crucial role in amplicon-based targeted re-sequencing and specific procedures in data analysis of amplicon data should be adopted. Although a consensus has been established in the elimination of primer sequences from aligned data (e.g., via primer sequence trimming or soft clipping), more complex configurations need to be managed in order to increase the retrieved information from available data. Our method shows how to manage one of these complex configurations, when two mutations occur.

A BioBrick™-Compatible Vector for Allelic Replacement Using the XylE Gene as Selection Marker.

BACKGROUND: Circular plasmid-mediated homologous recombination is commonly used for marker-less allelic replacement, exploiting the endogenous recombination machinery of the host. Common limitations of existing methods include high false positive rates due to mutations in counter-selection genes, and limited applicability to specific strains or growth media. Finally, solutions compatible with physical standards, such as the BioBrick™, are not currently available, although they proved to be successful in the design of other replicative or integrative plasmids. FINDINGS: We illustrate pBBknock, a novel BioBrick™-compatible vector for allelic replacement in Escherichia coli. It includes a temperature-sensitive replication origin and enables marker-less genome engineering via two homologous recombination events. Chloramphenicol resistance allows positive selection of clones after the first event, whereas a colorimetric assay based on the xylE gene provides a simple way to screen clones in which the second recombination event occurs. Here we successfully use pBBknock to delete the lactate dehydrogenase gene in E. coli W, a popular host used in metabolic engineering. CONCLUSIONS: Compared with other plasmid-based solutions, pBBknock has a broader application range, not being limited to specific strains or media. We expect that pBBknock will represent a versatile solution both for practitioners, also among the iGEM competition teams, and for research laboratories that use BioBrick™-based assembly procedures.

Experimental measurements and mathematical modeling of biological noise arising from transcriptional and translational regulation of basic synthetic gene circuits.

The small number of molecules, unevenly distributed within an isogenic cell population, makes gene expression a noisy process, and strategies have evolved to deal with this variability in protein concentration and to limit its impact on cellular behaviors. As translational efficiency has a major impact on biological noise, a possible strategy to control noise is to regulate gene expression processes at the post-transcriptional level. In this study, fluctuations in the concentration of a green fluorescent protein were compared, at the single cell level, upon transformation of an isogenic bacterial cell population with synthetic gene circuits implementing either a transcriptional or a post-transcriptional control of gene expression. Experimental measurements showed that protein variability is lower under post-transcriptional control, when the same average protein concentrations are compared. This effect is well reproduced by stochastic simulations, supporting the hypothesis that noise reduction is due to the control mechanism acting on the efficiency of translation. Similar strategies are likely to play a role in noise reduction in natural systems and to be useful for controlling noise in synthetic biology applications.

Cardiovascular Disease Burden, Mortality, and Sudden Death Risk in Epilepsy: A UK Biobank Study.

BACKGROUND: Sudden death is the leading cause of mortality in medically refractory epilepsy. Middle-aged persons with epilepsy (PWE) are under investigated regarding their mortality risk and burden of cardiovascular disease (CVD). METHODS: Using UK Biobank, we identified 7786 (1.6%) participants with diagnoses of epilepsy and 6,171,803 person-years of follow-up (mean 12.30 years, standard deviation 1.74); 566 patients with previous histories of stroke were excluded. The 7220 PWE comprised the study cohort with the remaining 494,676 without epilepsy as the comparator group. Prevalence of CVD was determined using validated diagnostic codes. Cox proportional hazards regression was used to assess all-cause mortality and sudden death risk. RESULTS: Hypertension, coronary artery disease, heart failure, valvular heart disease, and congenital heart disease were more prevalent in PWE. Arrhythmias including atrial fibrillation/flutter (12.2% vs 6.9%; P < 0.01), bradyarrhythmias (7.7% vs 3.5%; P < 0.01), conduction defects (6.1% vs 2.6%; P < 0.01), and ventricular arrhythmias (2.3% vs 1.0%; P < 0.01), as well as cardiac implantable electric devices (4.6% vs 2.0%; P < 0.01) were more prevalent in PWE. PWE had higher adjusted all-cause mortality (hazard ratio [HR], 3.9; 95% confidence interval [CI], 3.01-3.39), and sudden death-specific mortality (HR, 6.65; 95% CI, 4.53-9.77); and were almost 2 years younger at death (68.1 vs 69.8; P < 0.001). CONCLUSIONS: Middle-aged PWE have increased all-cause and sudden death-specific mortality and higher burden of CVD including arrhythmias and heart failure. Further work is required to elucidate mechanisms underlying all-cause mortality and sudden death risk in PWE of middle age, to identify prognostic biomarkers and develop preventative therapies in PWE.

Chromoanagenesis of chromosome 22 in a subject with obesity and borderline cognitive performance.

Chromoanagenesis events consist of complex chromosome rearrangements with multiple breakpoints in one or few chromosomes. Mechanisms of chromoanagenesis are split into three major groups: chromothripsis, chromoanasynthesis and chromoplexy. This study aims to delineate a chromoanagenesis event at the level of chromosome 22 in an individual showing obesity and borderline cognitive performance as major disturbances. The proband and his parents were subjected to conventional karyotyping, CGH array and whole genomic sequencing (WGS). By conventional karyotyping a "de novo" pericentric inversion of chromosome 22 was identified. CGH array identified several imbalances (either deletions or duplications) in the long arm of chromosome 22; the largest is a 4.5 Mb duplication at 22q12.1-22q1.3. The detection of extensive duplications would suggest the occurrence of a chromoanasynthesis event. WGS, in addition to the structural alterations identified by karyotyping and CGH array, revealed two translocations from chromosome 22 to chromosomes 6 and 21 as well as a heterozygous pathogenetic variant of ALMS1 gene; the latter could have contributed to the obesity of our patient. The pericentric inversion induces loss of initial part of TCF20 gene including the 5' regulatory region and the first, noncoding, exon. Heterozygous loss-of-function mutations of TCF20 gene have been found in patients with autism spectrum disorder or intellectual disability, some of them presenting obesity. It is, therefore, possible that disruption of TCF20 gene structure would contribute to a fraction of the patient's phenotype.

Characterization of an inducible promoter in different DNA copy number conditions.

BACKGROUND: The bottom-up programming of living organisms to implement novel user-defined biological capabilities is one of the main goals of synthetic biology. Currently, a predominant problem connected with the construction of even simple synthetic biological systems is the unpredictability of the genetic circuitry when assembled and incorporated in living cells. Copy number, transcriptional/translational demand and toxicity of the DNA-encoded functions are some of the major factors which may lead to cell overburdening and thus to nonlinear effects on system output. It is important to disclose the linearity working boundaries of engineered biological systems when dealing with such phenomena. RESULTS: The output of an N-3-oxohexanoyl-L-homoserine lactone (HSL)-inducible RFP-expressing device was studied in Escherichia coli in different copy number contexts, ranging from 1 copy per cell (integrated in the genome) to hundreds (via multicopy plasmids). The system is composed by a luxR constitutive expression cassette and a RFP gene regulated by the luxI promoter, which is activated by the HSL-LuxR complex. System output, in terms of promoter activity as a function of HSL concentration, was assessed relative to the one of a reference promoter in identical conditions by using the Relative Promoter Units (RPU) approach. Nonlinear effects were observed in the maximum activity, which is identical in single and low copy conditions, while it decreases for higher copy number conditions. In order to properly compare the luxI promoter strength among all the conditions, a mathematical modeling approach was used to relate the promoter activity to the estimated HSL-LuxR complex concentration, which is the actual activator of transcription. During model fitting, a correlation between the copy number and the dissociation constant of HSL-LuxR complex and luxI promoter was observed. CONCLUSIONS: Even in a simple inducible system, nonlinear effects are observed and non-trivial data processing is necessary to fully characterize its operation. The in-depth analysis of model systems like this can contribute to the advances in the synthetic biology field, since increasing the knowledge about linearity and working boundaries of biological phenomena could lead to a more rational design of artificial systems, also through mathematical models, which, for example, have been used here to study hard-to-predict interactions.

A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization.

Genomic variant interpretation is a critical step of the diagnostic procedure, often supported by the application of tools that may predict the damaging impact of each variant or provide a guidelines-based classification. We propose the application of Machine Learning methodologies, in particular Penalized Logistic Regression, to support variant classification and prioritization. Our approach combines ACMG/AMP guidelines for germline variant interpretation as well as variant annotation features and provides a probabilistic score of pathogenicity, thus supporting the prioritization and classification of variants that would be interpreted as uncertain by the ACMG/AMP guidelines. We compared different approaches in terms of variant prioritization and classification on different datasets, showing that our data-driven approach is able to solve more variant of uncertain significance (VUS) cases in comparison with guidelines-based approaches and in silico prediction tools.

Phenotypic Variation in Two Siblings Affected with Shwachman-Diamond Syndrome: The Use of Expert Variant Interpreter (eVai) Suggests Clinical Relevance of a Variant in the KMT2A Gene.

Introduction. Shwachman-Diamond Syndrome (SDS) is an autosomal-recessive disorder characterized by neutropenia, pancreatic exocrine insufficiency, skeletal dysplasia, and an increased risk for leukemic transformation. Biallelic mutations in the SBDS gene have been found in about 90% of patients. The clinical spectrum of SDS in patients is wide, and variability has been noticed between different patients, siblings, and even within the same patient over time. Herein, we present two SDS siblings (UPN42 and UPN43) carrying the same SBDS mutations and showing relevant differences in their phenotypic presentation. Study aim. We attempted to understand whether other germline variants, in addition to SBDS, could explain some of the clinical variability noticed between the siblings. Methods. Whole-exome sequencing (WES) was performed. Human Phenotype Ontology (HPO) terms were defined for each patient, and the WES data were analyzed using the eVai and DIVAs platforms. Results. In UPN43, we found and confirmed, using Sanger sequencing, a novel de novo variant (c.10663G > A, p.Gly3555Ser) in the KMT2A gene that is associated with autosomal-dominant Wiedemann−Steiner Syndrome. The variant is classified as pathogenic according to different in silico prediction tools. Interestingly, it was found to be related to some of the HPO terms that describe UPN43. Conclusions. We postulate that the KMT2A variant found in UPN43 has a concomitant and co-occurring clinical effect, in addition to SBDS mutation. This dual molecular effect, supported by in silico prediction, could help to understand some of the clinical variations found among the siblings. In the future, these new data are likely to be useful for personalized medicine and therapy for selected cases.

Differential Neuropathology, Genetics, and Transcriptomics in Two Kindred Cases with Alzheimer's Disease and Lewy Body Dementia.

Alzheimer's disease (AD) and Lewy body dementia (LBD) are two different forms of dementia, but their pathology may involve the same cortical areas with overlapping cognitive manifestations. Nonetheless, the clinical phenotype is different due to the topography of the lesions driven by the different underlying molecular processes that arise apart from genetics, causing diverse neurodegeneration. Here, we define the commonalities and differences in the pathological processes of dementia in two kindred cases, a mother and a son, who developed classical AD and an aggressive form of AD/LBD, respectively, through a neuropathological, genetic (next-generation sequencing), and transcriptomic (RNA-seq) comparison of four different brain areas. A genetic analysis did not reveal any pathogenic variants in the principal AD/LBD-causative genes. RNA sequencing highlighted high transcriptional dysregulation within the substantia nigra in the AD/LBD case, while the AD case showed lower transcriptional dysregulation, with the parietal lobe being the most involved brain area. The hippocampus (the most degenerated area) and basal ganglia (lacking specific lesions) expressed the lowest level of dysregulation. Our data suggest that there is a link between transcriptional dysregulation and the amount of tissue damage accumulated across time, assessed through neuropathology. Moreover, we highlight that the molecular bases of AD and LBD follow very different pathways, which underlie their neuropathological signatures. Indeed, the transcriptome profiling through RNA sequencing may be an important tool in flanking the neuropathological analysis for a deeper understanding of AD and LBD pathogenesis.

Extracellular Vesicles Derived From Plasma of Patients With Neurodegenerative Disease Have Common Transcriptomic Profiling.

OBJECTIVES: There is a lack of effective biomarkers for neurodegenerative diseases (NDs) such as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and frontotemporal dementia. Extracellular vesicle (EV) RNA cargo can have an interesting potential as a non-invasive biomarker for NDs. However, the knowledge about the abundance of EV-mRNAs and their contribution to neurodegeneration is not clear. METHODS: Large and small EVs (LEVs and SEVs) were isolated from plasma of patients and healthy volunteers (control, CTR) by differential centrifugation and filtration, and RNA was extracted. Whole transcriptome was carried out using next generation sequencing (NGS). RESULTS: Coding RNA (i.e., mRNA) but not long non-coding RNAs (lncRNAs) in SEVs and LEVs of patients with ALS could be distinguished from healthy CTRs and from other NDs using the principal component analysis (PCA). Some mRNAs were found in commonly deregulated between SEVs of patients with ALS and frontotemporal dementia (FTD), and they were classified in mRNA processing and splicing pathways. In LEVs, instead, one mRNA and one antisense RNA (i.e., MAP3K7CL and AP003068.3) were found to be in common among ALS, FTD, and PD. No deregulated mRNAs were found in EVs of patients with AD. CONCLUSION: Different RNA regulation occurs in LEVs and SEVs of NDs. mRNAs and lncRNAs are present in plasma-derived EVs of NDs, and there are common and specific transcripts that characterize LEVs and SEVs from the NDs considered in this study.

COVID-19 patients and Dementia: Frontal cortex transcriptomic data.

Since the association of SARS-Cov-2 infection with Nervous System (NS) manifestations, we performed RNA-sequencing analysis in Frontal Cortex of COVID-19 positive or negative individuals and affected or not by Dementia individuals. We examined gene expression differences in individuals with COVID-19 and Dementia compared to Dementia only patients by collecting transcript counts in each sample and performing Differential Expression analysis. We found eleven genes satisfying our significance criteria, all of them being protein coding genes. These data are suitable for integration with supplemental samples and for analysis according to different individuals' classification. Also, differential expression evaluation may be implemented with other scientific purposes, such as research of unannotated genes, mRNA splicing and genes isoforms. The analysis of Differential Expressed genes in COVID-19 positive patients compared to non-COVID-19 patients is published in: S. Gagliardi, E.T. Poloni, C. Pandini, M. Garofalo, F. Dragoni, V. Medici, A. Davin, S.D. Visonà, M. Moretti, D. Sproviero, O. Pansarasa, A. Guaita, M. Ceroni, L. Tronconi, C. Cereda, Detection of SARS-CoV-2 genome and whole transcriptome sequencing in frontal cortex of COVID-19 patients., Brain. Behav. Immun. (2021). https://doi.org/10.1016/j.bbi.2021.05.012.

A Synthetic Close-Loop Controller Circuit for the Regulation of an Extracellular Molecule by Engineered Bacteria.

Feedback control is ubiquitous in biological systems. It can also play a crucial role in the design of synthetic circuits implementing novel functions in living systems, to achieve self-regulation of gene expression, noise reduction, rise time decrease, or adaptive pathway control. Despite in vitro, in vivo, and ex vivo implementations have been successfully reported, the design of biological close-loop systems with quantitatively predictable behavior is still a major challenge. In this work, we tested a model-based bottom-up design of a synthetic close-loop controller in engineered Escherichia coli, aimed to automatically regulate the concentration of an extracellular molecule, N-(3-oxohexanoyl)-L-homoserine lactone (HSL), by rewiring the elements of heterologous quorum sensing/quenching networks. The synthetic controller was successfully constructed and experimentally validated. Relying on mathematical model and experimental characterization of individual regulatory parts and enzymes, we evaluated the predictability of the interconnected system behavior in vivo. The culture was able to reach an HSL steady-state level of 72 nM, accurately predicted by the model, and showed superior capabilities in terms of robustness against cell density variation and disturbance rejection, compared with a corresponding open-loop circuit. This engineering-inspired design approach may be adopted for the implementation of other close-loop circuits for different applications and contribute to decreasing trial-and-error steps.

RNA-Seq profiling in peripheral blood mononuclear cells of amyotrophic lateral sclerosis patients and controls.

Coding and long non-coding RNA (lncRNA) metabolism is now revealing its crucial role in Amyotrophic Lateral Sclerosis (ALS) pathogenesis. In this work, we present a dataset obtained via Illumina RNA-seq analysis on Peripheral Blood Mononuclear Cells (PBMCs) from sporadic and mutated ALS patients (mutations in FUS, TARDBP, SOD1 and VCP genes) and healthy controls. This dataset allows the whole-transcriptome characterization of PBMCs content, both in terms of coding and non-coding RNAs, in order to compare the disease state to the healthy controls, both for sporadic patients and for mutated patients. Our dataset is a starting point for the omni-comprehensive analysis of coding and lncRNAs, from an easy to withdraw, manage and store tissue that shows to be a suitable model for RNA profiling in ALS.