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Background and Objectives: The major histocompatibility complex (MHC) locus has a predominant role in the genetic predisposition to multiple sclerosis (MS), with 32 associations found to be involved. We aimed to investigate the impact of MHC MS-risk alleles on T-cell repertoire in patients with MS., Methods: We studied 161 untreated patients with relapsing-remitting MS for whom Class I and II human leukocyte antigen (HLA) alleles were inferred from whole-genome genotyping data, and T-cell receptor (TCR) CDR3 sequences were obtained through next-generation sequencing. T-cell repertoire features including diversity, public clones, and architecture were evaluated., Results: We identified 5 MS-risk loci associated with TCR diversity: HLA-DRB1*15:01 (7.65 × 10 -3 ), rs9271366 (1.96 × 10 -3 ), rs766848979 A (1.89 × 10 -2 ), rs9277626 (2.95 × 10 -2 ), and rs11751659 (1.92 × 10 -2 ), with evidence of expanded clonotypes in carriers of risk alleles. Moreover, HLA-DRB1*15:01 (4.99 × 10 -3 ), rs9271366 (6.54 × 10 -3 ), rs1049079 C (4.37 × 10 -2 ), AA DQΒ1 position -5 L (1.05 × 10 -3 ), and AA DQΒ1 position 221 Q (9.39 × 10 -4 ) showed an association with the CDR3 aminoacidic sequence architecture, suggesting an impact on the antigen recognition breadth as well. Evaluating the sharing of clones across MS-risk allele carrier individuals revealed the presence of highly shared clonotypes predicted to target viral antigens, including Epstein-Barr virus., Discussion: Our study supports the association between MHC-risk alleles and macrofeatures of the T-cell repertoire in the context of MS. Further studies are needed to understand the underlying molecular mechanisms., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

A personalized approach is strongly advocated for treatment selection in Multiple Sclerosis patients due to the high number of available drugs. Machine learning methods proved to be valuable tools in the context of precision medicine. In the present work, we applied machine learning methods to identify a combined clinical and genetic signature of response to fingolimod that could support the prediction of drug response. Two cohorts of fingolimod-treated patients from Italy and France were enrolled and divided into training, validation, and test set. Random forest training and robust feature selection were performed in the first two sets respectively, and the independent test set was used to evaluate model performance. A genetic-only model and a combined clinical-genetic model were obtained. Overall, 381 patients were classified according to the NEDA-3 criterion at 2 years; we identified a genetic model, including 123 SNPs, that was able to predict fingolimod response with an AUROC= 0.65 in the independent test set. When combining clinical data, the model accuracy increased to an AUROC= 0.71. Integrating clinical and genetic data by means of machine learning methods can help in the prediction of response to fingolimod, even though further studies are required to definitely extend this approach to clinical applications.

While the role of common genetic variants in multiple sclerosis (MS) has been elucidated in large genome-wide association studies, the contribution of rare variants to the disease remains unclear. Herein, a whole-genome sequencing study in four affected and four healthy relatives of a consanguineous Italian family identified a novel missense c.1801T > C (p.S601P) variant in the GRAMD1B gene that is shared within MS cases and resides under a linkage peak (LOD: 2.194). Sequencing GRAMD1B in 91 familial MS cases revealed two additional rare missense and two splice-site variants, two of which (rs755488531 and rs769527838) were not found in 1000 Italian healthy controls. Functional studies demonstrated that GRAMD1B , a gene with unknown function in the central nervous system (CNS), is expressed by several cell types, including astrocytes, microglia and neurons as well as by peripheral monocytes and macrophages. Notably, GRAMD1B was downregulated in vessel-associated astrocytes of active MS lesions in autopsied brains and by inflammatory stimuli in peripheral monocytes, suggesting a possible role in the modulation of inflammatory response and disease pathophysiology.

Multiple sclerosis (MS) is a complex disease of the central nervous system for which human leukocyte antigen (HLA) alleles are major contributors to susceptibility. Several investigations have focused on the relationship between HLA and clinical parameters, while few studies have evaluated its correlation with brain magnetic resonance imaging (MRI) measures. We investigated the association between the HLA genetic burden (HLAGB), originating from the most updated HLA alleles associated with MS, and neuroimaging endophenotypes, with a specific focus on brain atrophy metrics. A monocentric Italian cohort of 334 MS patients with imputed HLA alleles and cross-sectional volumetric measures of white matter (WM), gray matter (GM), hippocampus, thalamus and T2-hyperintense lesions was investigated. Linear regression models with covariate adjustment were fitted for each metric. We detected no effect of HLAGB on WM and GM volumes. Interestingly, we found a marginal correlation between higher HLAGB and lower hippocampal volume (β = -0.142, p = 0.063) and a nominal association between higher HLAGB and lower thalamic volume (β = -0.299, p = 0.047). No association was found with T2 lesion volumes. The putative impact of higher HLAGB on hippocampus and thalamus suggests, if replicated in independent cohorts, a possible cumulative contribution of HLA risk loci on brain volumetric traits linked to clinical deficits in MS.

Multiple sclerosis (MS) is an inflammatory neurodegenerative disorder of the central nervous system (CNS). The migration of immune cells into the CNS is essential for its development, and plasma membrane molecules play an important role in triggering and maintaining the inflammation. We previously identified ninjurin2, a plasma membrane protein encoded by NINJ2 gene, as involved in the occurrence of relapse under Interferon-β treatment in MS patients. The aim of the present study was to investigate the involvement of NINJ2 in inflammatory conditions and in the migration of monocytes through the blood-brain barrier (BBB). We observed that NINJ2 is downregulated in monocytes and in THP-1 cells after stimulation with the pro-inflammatory cytokine LPS, while in hCMEC/D3 cells, which represent a surrogate of the BBB, LPS stimulation increases its expression. We set up a transmigration assay using an hCMEC/D3 transwell-based model, finding a higher transmigration rate of monocytes from MS subjects compared to healthy controls (HCs) in the case of an activated hCMEC/D3 monolayer. Moreover, a positive correlation between NINJ2 expression in monocytes and monocyte migration rate was observed. Overall, our results suggest that ninjurin2 could be involved in the transmigration of immune cells into the CNS in pro-inflammatory conditions. Further experiments are needed to elucidate the exact molecular mechanisms., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Although inflammation appears to play a role in neurolymphomatosis (NL), the mechanisms leading to degeneration in the peripheral nervous system are poorly understood. The purpose of this exploratory study was to identify molecular pathways underlying NL pathogenesis, combining clinical and neuropathological investigation with gene expression (GE) studies. We characterized the clinical and pathological features of eight patients with NL. We further analysed GE changes in sural nerve biopsies obtained from a subgroup of NL patients (n=3) and thirteen patients with inflammatory neuropathies as neuropathic controls. Based on the neuropathic symptoms and signs, NL patients were classified into three forms of neuropathy: chronic symmetrical sensorimotor polyneuropathy (SMPN, n=3), multiple mononeuropathy (MN, n=4) and acute motor-sensory axonal neuropathy (AMSAN, n=1). Predominantly diffuse malignant cells infiltration of epineurium was present in chronic SMPN, whereas endoneurial perivascular cells invasion was observed in MN. In contrast, diffuse endoneurium malignant cells localization occurred in AMSAN. We identified alterations in the expression of 1266 genes, with 115 up-regulated and 1151 down-regulated genes, which were mainly associated with ribosomal proteins (RP) and olfactory receptors (OR) signaling pathways, respectively. Among the top up-regulated genes were actin alpha 1 skeletal muscle (ACTA1) and desmin (DES). Similarly, in NL nerves ACTA1, DES and several RPs were highly expressed, associated with endothelial cells and pericytes abnormalities. Peripheral nerve involvement may be due to conversion towards a more aggressive phenotype, potentially explaining the poor prognosis. The candidate genes reported in this study may be a source of clinical biomarkers for NL., Competing Interests: MF is Editor-in-Chief of the Journal of Neurology and Associate Editor of Neurological Sciences, received compensation for consulting services and/or speaking activities from Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda and Teva Pharmaceutical Industries, and receives research support from Biogen Idec, Merck-Serono, Novartis, Teva Pharmaceutical Industries, Roche, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla and ARiSLA (Fondazione Italiana di Ricerca per la SLA). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Cerri, Gentile, Clarelli, Santoro, Falzone, Dina, Romano, Domi, Pozzi, Fazio, Podini, Sorosina, Carrera, Esposito, Riva, Briani, Cavallaro, Filippi and Quattrini.)

Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotic's mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that both bacteriostatic and bactericidal agents can be equally effective at suppressing the selection of resistant mutants, but that key determinants of resistance selection are the relationships between the number of drug-inactivated targets within a cell and the rates of cellular growth and death. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strain's minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Background: Rehabilitation is fundamental for progressive multiple sclerosis (MS), but predictive biomarkers of motor recovery are lacking, making patient selection difficult. Motor recovery depends on synaptic plasticity, in which the Brain-Derived Neurotrophic Factor (BDNF) is a key player, through its binding to the Neurotrophic-Tyrosine Kinase-2 (NTRK2) receptor. Therefore, genetic polymorphisms in the BDNF pathway may impact motor recovery. The most well-known polymorphism in BDNF gene (rs6265) causes valine to methionine substitution (Val66Met) and it influences memory and motor learning in healthy individuals and neurodegenerative diseases. To date, no studies have explored whether polymorphisms in BDNF or NTRK2 genes may impact motor recovery in MS., Objectives: To assess whether genetic variants in BDNF and NTRK2 genes affect motor recovery after rehabilitation in progressive MS., Methods: The association between motor recovery after intensive neurorehabilitation and polymorphisms in BDNF (rs6265) and NTKR2 receptor (rs2289656 and rs1212171) was assessed using Six-Minutes-Walking-Test (6MWT), 10-Metres-Test (10MT) and Nine-Hole-Peg-Test (9HPT) in 100 progressive MS patients., Results: We observed greater improvement at 6MWT after rehabilitation in carriers of the BDNF Val66Met substitution, compared to BDNF Val homozygotes ( p = 0.024). No significant association was found for 10MT and 9HPT. NTRK2 polymorphisms did not affect the results of motor function tests., Conclusion: BDNF Val66Met was associated with walking function improvement after rehabilitation in progressive MS patients. This result is in line with previous evidence showing a protective effect of Val66Met substitution on brain atrophy in MS. Larger studies are needed to explore its potential as a predictive biomarker of rehabilitation outcome., Competing Interests: LL reports personal fees for speaking or consulting activities from Roche, Merck, Bristol Myers Squibb, Med-ex learning, outside the submitted work. FE received compensation for consulting services and/or speaking activities from Novartis, Sanofi Genzyme, Almirall, Teva, and Merck-Serono. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Giordano, Clarelli, Cannizzaro, Mascia, Santoro, Sorosina, Ferrè, Leocani and Esposito.)

Aim: To investigate the transcriptional changes induced by Fingolimod (FTY) in T cells of relapsing remitting multiple sclerosis patients. Patients & methods: Transcriptomic changes after 6 months of FTY therapy were evaluated on T cells from 24 relapsing remitting multiple sclerosis patients through RNA-sequencing, followed by technical validation and pathway analysis. Results: Among differentially expressed genes, CX3CR1 and CCR7 resulted strongly up- and downregulated, respectively. Two relevant genes were validated with quantitative PCR and we largely confirmed findings from two previous microarray-based studies with similar design. Pathway analysis pointed to an involvement of processes related to immune function and cell migration. Conclusion: Our data support the evidence that FTY induces major transcriptional changes in genes involved in immune response and cell trafficking in T lymphocytes.

Background: As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allow non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria., Results: In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis bacteria using our vCOMBAT tool., Conclusions: The vCOMBAT online tool is publicly available at https://combat-bacteria.org/ ., (© 2021. The Author(s).)

Genome-wide association studies identified over 200 risk loci for multiple sclerosis (MS) focusing on common variants, which account for about 50% of disease heritability. The goal of this study was to investigate whether low-frequency and rare functional variants, located in MS-established associated loci, may contribute to disease risk in a relatively homogeneous population, testing their cumulative effect (burden) with gene-wise tests. We sequenced 98 genes in 588 Italian patients with MS and 408 matched healthy controls (HCs). Variants were selected using different filtering criteria based on allelic frequency and in silico functional impacts. Genes showing a significant burden (n = 17) were sequenced in an independent cohort of 504 MS and 504 HC. The highest signal in both cohorts was observed for the disruptive variants (stop-gain, stop-loss, or splicing variants) located in EFCAB13 , a gene coding for a protein of an unknown function ( p < 10 -4 ). Among these variants, the minor allele of a stop-gain variant showed a significantly higher frequency in MS versus HC in both sequenced cohorts ( p = 0.0093 and p = 0.025), confirmed by a meta-analysis on a third independent cohort of 1298 MS and 1430 HC ( p = 0.001) assayed with an SNP array. Real-time PCR on 14 heterozygous individuals for this variant did not evidence the presence of the stop-gain allele, suggesting a transcript degradation by non-sense mediated decay, supported by the evidence that the carriers of the stop-gain variant had a lower expression of this gene ( p = 0.0184). In conclusion, we identified a novel low-frequency functional variant associated with MS susceptibility, suggesting the possible role of rare/low-frequency variants in MS as reported for other complex diseases., Competing Interests: MF is Editor-in-Chief of the Journal of Neurology, Associate Editor of Human Brain Mapping, Associate Editor of Radiology, and Associate Editor of Neurological Sciences; received compensation for consulting services and/or speaking activities from Alexion, Almirall, Bayer, Biogen, Celgene, Eli Lilly, Genzyme, Merck-Serono, Novartis, Roche, Sanofi, Takeda, and Teva Pharmaceutical Industries; and receives research support from Biogen Idec, Merck-Serono, Novartis, Roche, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA). VM received compensation for speaking and/or for consultancy and support for travel expenses and participation in Congresses from Biogen, Merck-Serono, Novartis, Roche, Genzyme and Teva Pharmaceutical Industries. FMB has received compensation for consulting services and/or speaking activities from Teva Pharmaceutical Industries, Sanofi Genzyme, Merck-Serono, Biogen Idec, Roche, Medday, Excemed, and received research support from Merck, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla and Fondazione Cariplo. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Clarelli, Barizzone, Mangano, Zuccalà, Basagni, Anand, Sorosina, Mascia, Santoro, Guerini, Virgilio, Gallo, Pizzino, Comi, Martinelli, Comi, De Bellis, Leone, Filippi, Esposito, Bordoni, Martinelli Boneschi and D'Alfonso.)

Antibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) that can quantitatively predict antibiotic dose-response relationships. Our goal is dual: We address a fundamental biological question and investigate how drug-target binding shapes antibiotic action. We also create a tool that can predict antibiotic efficacy a priori. COMBAT requires measurable biochemical parameters of drug-target interaction and can be directly fitted to time-kill curves. As a proof-of-concept, we first investigate the utility of COMBAT with antibiotics belonging to the widely used quinolone class. COMBAT can predict antibiotic efficacy in clinical isolates for quinolones from drug affinity (R2>0.9). To further challenge our approach, we also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We overexpress target molecules to infer changes in antibiotic-target binding from changes in antimicrobial efficacy of ciprofloxacin with 92-94% accuracy. To test the generality of our approach, we use the beta-lactam ampicillin to predict target molecule occupancy at MIC from antimicrobial action with 90% accuracy. Finally, we apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations. Using ciprofloxacin and ampicillin as well defined test cases, our work demonstrates that drug-target binding is a major predictor of bacterial responses to antibiotics. This is surprising because antibiotic action involves many additional effects downstream of drug-target binding. In addition, COMBAT provides a framework to inform optimal antibiotic dose levels that maximize efficacy and minimize the rise of resistant mutants., Competing Interests: The authors have declared that no competing interests exist.

Optimizing drug therapies for any disease requires a solid understanding of pharmacokinetics (the drug concentration at a given time point in different body compartments) and pharmacodynamics (the effect a drug has at a given concentration). Mathematical models are frequently used to infer drug concentrations over time based on infrequent sampling and/or in inaccessible body compartments. Models are also used to translate drug action from in vitro to in vivo conditions or from animal models to human patients. Recently, mathematical models that incorporate drug-target binding and subsequent downstream responses have been shown to advance our understanding and increase predictive power of drug efficacy predictions. We here discuss current approaches of modeling drug binding kinetics that aim at improving model-based drug development in the future. This in turn might aid in reducing the large number of failed clinical trials.

Bacterial heteroresistance (i.e., the co-existence of several subpopulations with different antibiotic susceptibilities) can delay the clearance of bacteria even with long antibiotic exposure. Some proposed mechanisms have been successfully described with mathematical models of drug-target binding where the mechanism's downstream of drug-target binding are not explicitly modeled and subsumed in an empirical function, connecting target occupancy to antibiotic action. However, with current approaches it is difficult to model mechanisms that involve multi-step reactions that lead to bacterial killing. Here, we have a dual aim: first, to establish pharmacodynamic models that include multi-step reaction pathways, and second, to model heteroresistance and investigate which molecular heterogeneities can lead to delayed bacterial killing. We show that simulations based on Gillespie algorithms, which have been employed to model reaction kinetics for decades, can be useful tools to model antibiotic action via multi-step reactions. We highlight the strengths and weaknesses of current models and Gillespie simulations. Finally, we show that in our models, slight normally distributed variances in the rates of any event leading to bacterial death can (depending on parameter choices) lead to delayed bacterial killing (i.e., heteroresistance). This means that a slowly declining residual bacterial population due to heteroresistance is most likely the default scenario and should be taken into account when planning treatment length.

Background: The acquisition of reliable tissue-specific RNA sequencing data from human skin biopsy represents a major advance in research. However, the complexity of the process of isolation of specific layers from fresh-frozen human specimen by laser capture microdissection, the abundant presence of skin nucleases and RNA instability remain relevant methodological challenges. We developed and optimized a protocol to extract RNA from layers of human skin biopsies and to provide satisfactory quality and amount of mRNA sequencing data., Results: The protocol includes steps of collection, embedding, freezing, histological coloration and relative optimization to preserve RNA extracted from specific components of fresh-frozen human skin biopsy of 14 subjects. Optimization of the protocol includes a preservation step in RNALater ® Solution, the control of specimen temperature, the use of RNase Inhibitors and the time reduction of the staining procedure. The quality of extracted RNA was measured using the percentage of fragments longer than 200 nucleotides (DV 200 ), a more suitable measurement for successful library preparation than the RNA Integrity Number (RIN). RNA was then enriched using the TruSeq ® RNA Access Library Prep Kit (Illumina ® ) and sequenced on HiSeq ®  2500 platform (Illumina ® ). Quality control on RNA sequencing data was adequate to get reliable data for downstream analysis., Conclusions: The described implemented and optimized protocol can be used for generating transcriptomics data on skin tissues, and it is potentially applicable to other tissues. It can be extended to multicenter studies, due to the introduction of an initial step of preservation of the specimen that allowed the shipment of biological samples.

Identifying optimal dosing of antibiotics has proven challenging-some antibiotics are most effective when they are administered periodically at high doses, while others work best when minimizing concentration fluctuations. Mechanistic explanations for why antibiotics differ in their optimal dosing are lacking, limiting our ability to predict optimal therapy and leading to long and costly experiments. We use mathematical models that describe both bacterial growth and intracellular antibiotic-target binding to investigate the effects of fluctuating antibiotic concentrations on individual bacterial cells and bacterial populations. We show that physicochemical parameters, e.g. the rate of drug transmembrane diffusion and the antibiotic-target complex half-life are sufficient to explain which treatment strategy is most effective. If the drug-target complex dissociates rapidly, the antibiotic must be kept constantly at a concentration that prevents bacterial replication. If antibiotics cross bacterial cell envelopes slowly to reach their target, there is a delay in the onset of action that may be reduced by increasing initial antibiotic concentration. Finally, slow drug-target dissociation and slow diffusion out of cells act to prolong antibiotic effects, thereby allowing for less frequent dosing. Our model can be used as a tool in the rational design of treatment for bacterial infections. It is easily adaptable to other biological systems, e.g. HIV, malaria and cancer, where the effects of physiological fluctuations of drug concentration are also poorly understood., Competing Interests: The authors have declared that no competing interests exist.

The aim of the present study is to investigate the molecular pathways underlying amyotrophic lateral sclerosis (ALS) pathogenesis within the peripheral nervous system. We analyzed gene expression changes in human motor nerve diagnostic biopsies obtained from eight ALS patients and seven patients affected by motor neuropathy as controls. An integrated transcriptomics and system biology approach was employed. We identified alterations in the expression of 815 genes, with 529 up-regulated and 286 down-regulated in ALS patients. Up-regulated genes clustered around biological process involving RNA processing and protein metabolisms. We observed a significant enrichment of up-regulated small nucleolar RNA transcripts (p = 2.68*10-11) and genes related to endoplasmic reticulum unfolded protein response and chaperone activity. We found a significant down-regulation in ALS of genes related to the glutamate metabolism. Interestingly, a network analysis highlighted HDAC2, belonging to the histone deacetylase family, as the most interacting node. While so far gene expression studies in human ALS have been performed in postmortem tissues, here specimens were obtained from biopsy at an early phase of the disease, making these results new in the field of ALS research and therefore appealing for gene discovery studies.

Sequencing large number of individuals, which is often needed for population genetics studies, is still economically challenging despite falling costs of Next Generation Sequencing (NGS). Pool-seq is an alternative cost- and time-effective option in which DNA from several individuals is pooled for sequencing. However, pooling of DNA creates new problems and challenges for accurate variant call and allele frequency (AF) estimation. In particular, sequencing errors confound with the alleles present at low frequency in the pools possibly giving rise to false positive variants. We sequenced 996 individuals in 83 pools (12 individuals/pool) in a targeted re-sequencing experiment. We show that Pool-seq AFs are robust and reliable by comparing them with public variant databases and in-house SNP-genotyping data of individual subjects of pools. Furthermore, we propose a simple filtering guideline for the removal of spurious variants based on the Kolmogorov-Smirnov statistical test. We experimentally validated our filters by comparing Pool-seq to individual sequencing data showing that the filters remove most of the false variants while retaining majority of true variants. The proposed guideline is fairly generic in nature and could be easily applied in other Pool-seq experiments.

Microarray platforms require analytical pipelines with modules for data pre-processing including data normalization, statistical analysis for identification of differentially expressed genes, cluster analysis, and functional annotation. We previously developed the Automated Microarray Data Analysis (AMDA, version 2.3.5) pipeline to process Affymetrix 3' IVT GeneChips. The availability of newer technologies that demand open-source tools for microarray data analysis has impelled us to develop an updated multi-platform version, AMDA 2.13. It includes additional quality control metrics, annotation-driven (annotation grade of Affymetrix NetAffx) and signal-driven (Inter-Quartile Range) gene filtering, and approaches to experimental design. To enhance understanding of biological data, differentially expressed genes have been mapped into KEGG pathways. Finally, a more stable and user-friendly interface was designed to integrate the requirements for different platforms. AMDA 2.13 allows the analysis of Affymetrix (cartridges and plates) and whole transcript probe design (Gene 1.0/1.1 ST and Exon 1.0 ST GeneChips), Illumina Bead Arrays, and one-channel Agilent 4×44 arrays. Relative to early versions, it supports various experimental designs and delivers more insightful biological understanding and up-to-date annotations.