Search

Skin cancer, primarily resulting from the abnormal growth of skin cells, is among the most common cancer types. In recent decades, the incidence of skin cancer cases worldwide has risen significantly (one in every three newly diagnosed cancer cases is a skin cancer). Such an increase can be attributed to changes in our social and lifestyle habits coupled with devastating man-made alterations to the global ecosystem. Despite such a notable increase, diagnosis of skin cancer is still challenging, which becomes critical as its early detection is crucial for increasing the overall survival rate. This calls for advancements of innovative computer-aided systems to assist medical experts with their decision making. In this context, there has been a recent surge of interest in machine learning (ML), in particular, deep neural networks (DNNs), to provide complementary assistance to expert physicians. While DNNs have a high processing capacity far beyond that of human experts, their outputs are deterministic, i.e., providing estimates without prediction confidence. Therefore, it is of paramount importance to develop DNNs with uncertainty-awareness to provide confidence in their predictions. Monte Carlo dropout (MCD) is vastly used for uncertainty quantification; however, MCD suffers from overconfidence and being miss calibrated. In this paper, we use MCD algorithm to develop an uncertainty-aware DNN that assigns high predictive entropy to erroneous predictions and enable the model to optimize the hyper-parameters during training, which leads to more accurate uncertainty quantification. We use two synthetic (two moons and blobs) and a real dataset (skin cancer) to validate our algorithm. Our experiments on these datasets prove effectiveness of our approach in quantifying reliable uncertainty. Our method achieved 85.65 ± 0.18 prediction accuracy, 83.03 ± 0.25 uncertainty accuracy, and 1.93 ± 0.3 expected calibration error outperforming vanilla MCD and MCD with loss enhanced bas

Mental health disorders may cause severe consequences for countries’ economies and health. Identifying early signs of these disorders is vital. The state-of-the-art research in identifying mental health disorder patterns from textual data, uses hand-labeled training sets, especially when a domain expert’s knowledge is required to analyze various symptoms in a patient. This task could be time-consuming and expensive. To address this challenge, in this paper, we study and analyze the various clinical and non-clinical approaches to identifying mental health disorders. We leverage the domain knowledge and expertise in cognitive science to build a domain-specific Knowledge Base for the mental health disorder concepts and patterns. We present a weaker form of supervision by facilitating and generating training data from a domain-specific Knowledge Base. We adopt a typical scenario for analyzing social media to identify depression symptoms from the textual content generated by social users.

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section.

Brain diseases, including tumors and mental and neurological disorders, seriously threaten the health and well-being of millions of people worldwide. Structural and functional neuroimaging modalities are commonly used by physicians to aid the diagnosis of brain diseases. In clinical settings, specialist doctors typically fuse the magnetic resonance imaging (MRI) data with other neuroimaging modalities for brain disease detection. As these two approaches offer complementary information, fusing these neuroimaging modalities helps physicians accurately diagnose brain diseases. Typically, fusion is performed between a functional and a structural neuroimaging modality. Because the functional modality can complement the structural modality information, thus improving the performance for the diagnosis of brain diseases by specialists. However, analyzing the fusion of neuroimaging modalities is difficult for specialist doctors. Deep Learning (DL) is a branch of artificial intelligence that has shown superior performances compared to more conventional methods in tasks such as brain disease detection from neuroimaging modalities. This work presents a comprehensive review paper in the field of brain disease detection from the fusion of neuroimaging modalities using DL models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), pretrained, generative adversarial networks (GANs), and Autoencoders (AEs). First, neuroimaging modalities and the need for fusion are discussed. Then, review papers published in the field of neuroimaging multimodalities using AI techniques are explored. Moreover, fusion levels based on DL methods, including input, layer, and decision, with related studies conducted on diagnosing brain diseases, are discussed. Other sections present the most important challenges for diagnosing brain diseases from the fusion of neuroimaging modalities. In the discussion section, the details of previous research on the fusion of neuroimaging modalit

Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O₂ Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

Bioinformatics and computational biology have significantly contributed to the generation of vast and important knowledge that can lead to great improvements and advancements in biology and its related fields. Over the past three decades, a wide range of tools and methods have been developed and proposed to enhance performance, diagnosis, and throughput while maintaining feasibility and convenience for users. Here, we propose a new user-friendly comprehensive tool called VIRMOTIF to analyze DNA sequences. VIRMOTIF brings different tools together as one package so that users can perform their analysis as a whole and in one place. VIRMOTIF is able to complete different tasks, including computing the number or probability of motifs appearing in DNA sequences, visualizing data using the matplotlib and heatmap libraries, and clustering data using four different methods, namely K-means, PCA, Mean Shift, and ClusterMap. VIRMOTIF is the only tool with the ability to analyze genomic motifs based on their frequency and representation (D-ratio) in a virus genome.

Analysis of cancer mutational signatures have been instrumental in identification of responsible endogenous and exogenous molecular processes in cancer. The quantitative approach used to deconvolute mutational signatures is becoming an integral part of cancer research. Therefore, development of a stand-alone tool with a user-friendly interface for analysis of cancer mutational signatures is necessary. In this manuscript we introduce CANCERSIGN, which enables users to identify 3-mer and 5-mer mutational signatures within whole genome, whole exome or pooled samples. Additionally, this tool enables users to perform clustering on tumor samples based on the proportion of mutational signatures in each sample. Using CANCERSIGN, we analysed all the whole genome somatic mutation datasets profiled by the International Cancer Genome Consortium (ICGC) and identified a number of novel signatures. By examining signatures found in exonic and non-exonic regions of the genome using WGS and comparing this to signatures found in WES data we observe that WGS can identify additional non-exonic signatures that are enriched in the non-coding regions of the genome while the deeper sequencing of WES may help identify weak signatures that are otherwise missed in shallower WGS data.

Clustering ensemble indicates to an approach in which a number of (usually weak) base clusterings are performed and their consensus clustering is used as the final clustering. Knowing democratic decisions are better than dictatorial decisions, it seems clear and simple that ensemble (here, clustering ensemble) decisions are better than simple model (here, clustering) decisions. But it is not guaranteed that every ensemble is better than a simple model. An ensemble is considered to be a better ensemble if their members are valid or high-quality and if they participate according to their qualities in constructing consensus clustering. In this paper, we propose a clustering ensemble framework that uses a simple clustering algorithm based on kmedoids clustering algorithm. Our simple clustering algorithm guarantees that the discovered clusters are valid. From another point, it is also guaranteed that our clustering ensemble framework uses a mechanism to make use of each discovered cluster according to its quality. To do this mechanism an auxiliary ensemble named reference set is created by running several kmeans clustering algorithms.

Background: Hi-C is a molecular biology technique to understand the genome spatial structure. However, data obtained from Hi-C experiments is biased. Therefore, several methods have been developed to model Hi-C data and identify significant interactions. Each method receives its own Hi-C data structure and only work on specific operating systems. Results: We introduce MHiC (Multi-function Hi-C data analysis tool), a tool to identify and visualize statistically signifiant interactions from Hi-C data. The MHiC tool (i) works on different operating systems, (ii) accepts various Hi-C data structures from different Hi-C analysis tools such as HiCUP or HiC-Pro, (iii) identify significant Hi-C interactions with GOTHiC, HiCNorm and Fit-Hi-C methods and (iv) visualizes interactions in Arc or Heatmap diagram. MHiC is an open-source tool which is freely available for download on https://github.com/MHi-C. Conclusions: MHiC is an integrated tool for the analysis of high-throughput chromosome conformation capture (Hi-C) data.

In this paper, we introduce an approach to discriminate unconventional images and their intelligent filtering. As the target data to this issue are huge and consequently, a handling approach might potentially be a very time consuming one, one of the major challenges to be solved by this introduced approach is its ability for dealing with large-scale datasets. A deep neural network might be a good option to resolve this challenge. It can provide a good accuracy while dealing with huge databases. In the proposed approach, the new architecture is introduced using a combination of AlexNet and LeNet architectures. It uses convolutional, polling and fully-connected layers. The results are tested on two large-scale datasets. These tests show that the introduced architecture is more accurate than the other recently developed methods in identifying unconventional images. The proposed approach may be used in different applications such as intelligent filtering of unconventional images or medical images analysis.

Modern next generation sequencing technologies produce huge amounts of genome-wide data that allow researchers to have a deeper understanding of genomics of organisms. Despite these huge amounts of data, our understanding of the transcriptional regulatory networks is still incomplete. Conformation dependent chromosome interaction maps technologies (Hi-C) have enabled us to detect elements in the genome which interact with each other and regulate the genes. Summarizing these interactions as a data network leads to investigation of the most important properties of the 3D genome structure such as gene co-expression networks. In this work, a Pareto-Based Multi-Objective Optimization algorithm is proposed to detect the co-expressed genomic regions in Hi-C interactions. The proposed method uses fixed sized genomic regions as the vertices of the graph. Number of read between two interacting genomic regions indicate the weight of each edge. The performance of our proposed algorithm was compared to the Multi-Objective PSO algorithm on five networks derived from cis genomic interactions in three Hi-C datasets (GM12878, CD34+ and ESCs). The experimental results show that our proposed algorithm outperforms Multi-Objective PSO technique in the identification of co-interacting genomic regions.

Background: Chromosome 22q11.2 is susceptible to genomic rearrangements and the most frequently reported involve deletions and duplications between low copy repeats LCR22A to LCR22D. Atypical nested deletions and duplications are rarer and can provide a valuable opportunity to investigate the dosage effects of a smaller subset of genes within the 22q11.2 genomic disorder region. Methods: We describe thirteen individuals from six families, each with atypical nested duplications within the central 22q11.2 region between LCR22B and LCR22D. We then compared the molecular and clinical data for patients from this study and the few reported atypical duplication cases, to the cases with larger typical duplications between LCR22A and LCR22D. Further, we analyzed genes with the nested region to identify candidates highly enriched in human brain tissues. Results: We observed that atypical nested duplications are heterogeneous in size, often familial, and associated with incomplete penetrance and highly variable clinical expressivity. We found that the nested atypical duplications are a possible risk factor for neurodevelopmental phenotypes, particularly for autism spectrum disorder (ASD), speech and language delay, and behavioral abnormalities. In addition, we analyzed genes within the nested region between LCR22B and LCR22D to identify nine genes (ZNF74, KLHL22, MED15, PI4KA, SERPIND1, CRKL, AIFM3, SLC7A4, and BCRP2) with enriched expression in the nervous system, each with unique spatiotemporal patterns in fetal and adult brain tissues. Interestingly, PI4KA is prominently expressed in the brain, and this gene is included either partially or completely in all of our subjects. Conclusion: Our findings confirm variable expressivity and incomplete penetrance for atypical nested 22q11.2 duplications and identify genes such as PI4KA to be directly relevant to brain development and disorder. We conclude that further work is needed to elucidate the basis of variable neurodevelopment

Background Chromosome 22q11.2 is susceptible to genomic rearrangements and the most frequently reported involve deletions and duplications between low copy repeats LCR22A to LCR22D. Atypical nested deletions and duplications are rarer and can provide a valuable opportunity to investigate the dosage effects of a smaller subset of genes within the 22q11.2 genomic disorder region. Methods We describe thirteen individuals from six families, each with atypical nested duplications within the central 22q11.2 region between LCR22B and LCR22D. We then compared the molecular and clinical data for patients from this study and the few reported atypical duplication cases, to the cases with larger typical duplications between LCR22A and LCR22D. Further, we analyzed genes with the nested region to identify candidates highly enriched in human brain tissues. Results We observed that atypical nested duplications are heterogeneous in size, often familial, and associated with incomplete penetrance and highly variable clinical expressivity. We found that the nested atypical duplications are a possible risk factor for neurodevelopmental phenotypes, particularly for autism spectrum disorder (ASD), speech and language delay, and behavioral abnormalities. In addition, we analyzed genes within the nested region between LCR22B and LCR22D to identify nine genes (ZNF74, KLHL22, MED15, PI4KA, SERPIND1, CRKL, AIFM3, SLC7A4, and BCRP2) with enriched expression in the nervous system, each with unique spatiotemporal patterns in fetal and adult brain tissues. Interestingly, PI4KA is prominently expressed in the brain, and this gene is included either partially or completely in all of our subjects. Conclusion Our findings confirm variable expressivity and incomplete penetrance for atypical nested 22q11.2 duplications and identify genes such as PI4KA to be directly relevant to brain development and disorder. We conclude that further work is needed to elucidate the basis of variable neurodevelopmental p

Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc. Background: Chromosome 22q11.2 is susceptible to genomic rearrangements and the most frequently reported involve deletions and duplications between low copy repeats LCR22A to LCR22D. Atypical nested deletions and duplications are rarer and can provide a valuable opportunity to investigate the dosage effects of a smaller subset of genes within the 22q11.2 genomic disorder region. Methods: We describe thirteen individuals from six families, each with atypical nested duplications within the central 22q11.2 region between LCR22B and LCR22D. We then compared the molecular and clinical data for patients from this study and the few reported atypical duplication cases, to the cases with larger typical duplications between LCR22A and LCR22D. Further, we analyzed genes with the nested region to identify candidates highly enriched in human brain tissues. Results: We observed that atypical nested duplications are heterogeneous in size, often familial, and associated with incomplete penetrance and highly variable clinical expressivity. We found that the nested atypical duplications are a possible risk factor for neurodevelopmental phenotypes, particularly for autism spectrum disorder (ASD), speech and language delay, and behavioral abnormalities. In addition, we analyzed genes within the nested region between LCR22B and LCR22D to identify nine genes (ZNF74, KLHL22, MED15, PI4KA, SERPIND1, CRKL, AIFM3, SLC7A4, and BCRP2) with enriched expression in the nervous system, each with unique spatiotemporal patterns in fetal and adult brain tissues. Interestingly, PI4KA is prominently expressed in the brain, and this gene is included either partially or completely in all of our subjects. Conclusion: Our findings confirm variable expressivity and incomplete penetrance for atypical nested 22q11.2 duplications and identify genes such as PI4KA to be directly relevant to brain development and disorder. We conclude that

The explosive growth of data in volume, velocity and diversity that are produced by medical applications has contributed to abundance of big data. Current solutions for efficient data storage and management cannot fulfill the needs of heterogeneous data. Therefore, by applying computational intelligence (CI) approaches in medical data helps get better management, faster performance and higher level of accuracy in detection. This paper aims to investigate the state-of-the-art of computational intelligence approaches in medical data and to categorize the existing CI techniques, used in medical fields, as single and hybrid. In addition, the techniques and methodologies, their limitations and performances are presented in this study. The limitations are addressed as challenges to obtain a set of requirements for Computational Intelligence Medical Data (CIMD) in establishing an efficient CIMD architectural design. The results show that on the one hand Support Vector Machine (SVM) and Artificial Immune Recognition System (AIRS) as a single based computational intelligence approach were the best methods in medical applications. On the other hand, the hybridization of SVM with other methods such as SVM-Genetic Algorithm (SVM-GA), SVM-Artificial Immune System (SVM-AIS), SVM-AIRS and fuzzy support vector machine (FSVM) had great performances achieving better results in terms of accuracy, sensitivity and specificity.

Recent advances in high-throughput technologies have provided an unprecedented opportunity to identify molecular markers of disease processes. This plethora of complex-omics data has simultaneously complicated the problem of extracting meaningful molecular signatures and opened up new opportunities for more sophisticated integrative and holistic approaches. In this era, effective integration of data-driven and knowledge-based approaches for biomarker identification has been recognised as key to improving the identification of high-performance biomarkers, and necessary for translational applications. Here, we have evaluated the role of circulating microRNA as a means of predicting the prognosis of patients with colorectal cancer, which is the second leading cause of cancer-related death worldwide. We have developed a multi-objective optimisation method that effectively integrates a data-driven approach with the knowledge obtained from the microRNA-mediated regulatory network to identify robust plasma microRNA signatures which are reliable in terms of predictive power as well as functional relevance. The proposed multi-objective framework has the capacity to adjust for conflicting biomarker objectives and to incorporate heterogeneous information facilitating systems approaches to biomarker discovery. We have found a prognostic signature of colorectal cancer comprising 11 circulating microRNAs. The identified signature predicts the patients’ survival outcome and targets pathways underlying colorectal cancer progression. The altered expression of the identified microRNAs was confirmed in an independent public data set of plasma samples of patients in early stage vs advanced colorectal cancer. Furthermore, the generality of the proposed method was demonstrated across three publicly available miRNA data sets associated with biomarker studies in other diseases.

Background: Pallister-Killian syndrome (PKS) is a rare multisystem developmental syndrome usually caused by mosaic tetrasomy of chromosome 12p that is known to be associated with neurological defects. Methods: We describe two patients with PKS, one of whom has bilateral perisylvian polymicrogyria (PMG), the other with macrocephaly, enlarged lateral ventricles and hypogenesis of the corpus callosum. We have also summarized the current literature describing brain abnormalities in PKS. Results: We reviewed available cases with intracranial scans (n = 93) and found a strong association between PKS and structural brain abnormalities (77.41%; 72/93). Notably, ventricular abnormalities (45.83%; 33/72), abnormalities of the corpus callosum (25.00%; 18/72) and cerebral atrophy (29.17%; 21/72) were the most frequently reported, while macrocephaly (12.5%; 9/72) and PMG (4.17%; 3/72) were less frequent. To further understand how 12p genes might be relevant to brain development, we identified 63 genes which are enriched in the nervous system. These genes display distinct temporal as well as region-specific expression in the brain, suggesting specific roles in neurodevelopment and disease. Finally, we utilized these data to define minimal critical regions on 12p and their constituent genes associated with atrophy, abnormalities of the corpus callosum, and macrocephaly in PKS. Conclusion: Our study reinforces the association between brain abnormalities and PKS, and documents a diverse neurogenetic basis for structural brain abnormalities and impaired function in children diagnosed with this rare disorder.

© 2017 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc. Background: Pallister-Killian syndrome (PKS) is a rare multisystem developmental syndrome usually caused by mosaic tetrasomy of chromosome 12p that is known to be associated with neurological defects. Methods: We describe two patients with PKS, one of whom has bilateral perisylvian polymicrogyria (PMG), the other with macrocephaly, enlarged lateral ventricles and hypogenesis of the corpus callosum. We have also summarized the current literature describing brain abnormalities in PKS. Results: We reviewed available cases with intracranial scans (n = 93) and found a strong association between PKS and structural brain abnormalities (77.41%; 72/93). Notably, ventricular abnormalities (45.83%; 33/72), abnormalities of the corpus callosum (25.00%; 18/72) and cerebral atrophy (29.17%; 21/72) were the most frequently reported, while macrocephaly (12.5%; 9/72) and PMG (4.17%; 3/72) were less frequent. To further understand how 12p genes might be relevant to brain development, we identified 63 genes which are enriched in the nervous system. These genes display distinct temporal as well as region-specific expression in the brain, suggesting specific roles in neurodevelopment and disease. Finally, we utilized these data to define minimal critical regions on 12p and their constituent genes associated with atrophy, abnormalities of the corpus callosum, and macrocephaly in PKS. Conclusion: Our study reinforces the association between brain abnormalities and PKS, and documents a diverse neurogenetic basis for structural brain abnormalities and impaired function in children diagnosed with this rare disorder.

Background Pallister-Killian syndrome (PKS) is a rare multisystem developmental syndrome usually caused by mosaic tetrasomy of chromosome 12p that is known to be associated with neurological defects. Methods We describe two patients with PKS, one of whom has bilateral perisylvian polymicrogyria (PMG), the other with macrocephaly, enlarged lateral ventricles and hypogenesis of the corpus callosum. We have also summarized the current literature describing brain abnormalities in PKS. Results We reviewed available cases with intracranial scans (n = 93) and found a strong association between PKS and structural brain abnormalities (77.41%; 72/93). Notably, ventricular abnormalities (45.83%; 33/72), abnormalities of the corpus callosum (25.00%; 18/72) and cerebral atrophy (29.17%; 21/72) were the most frequently reported, while macrocephaly (12.5%; 9/72) and PMG (4.17%; 3/72) were less frequent. To further understand how 12p genes might be relevant to brain development, we identified 63 genes which are enriched in the nervous system. These genes display distinct temporal as well as region-specific expression in the brain, suggesting specific roles in neurodevelopment and disease. Finally, we utilized these data to define minimal critical regions on 12p and their constituent genes associated with atrophy, abnormalities of the corpus callosum, and macrocephaly in PKS. Conclusion Our study reinforces the association between brain abnormalities and PKS, and documents a diverse neurogenetic basis for structural brain abnormalities and impaired function in children diagnosed with this rare disorder.

The low fidelity of HIV replication facilitates immune and drug escape. Some reverse transcriptase (RT) inhibitor drug-resistance mutations increase RT fidelity in biochemical assays but their effect during viral replication is unclear. We investigated the effect of RT mutations K65R, Q151N and V148I on SIV replication and fidelity in vitro, along with SIV replication in pigtailed macaques. SIVmac239-K65R and SIVmac239-V148I viruses had reduced replication capacity compared to wild-type SIVmac239. Direct virus competition assays demonstrated a rank order of wild-type>K65R>V148I mutants in terms of viral fitness. In single round in vitro-replication assays, SIVmac239-K65R demonstrated significantly higher fidelity than wild-type, and rapidly reverted to wild-type following infection of macaques. In contrast, SIVmac239-Q151N was replication incompetent in vitro and in pigtailed macaques. Thus, we showed that RT mutants, and specifically the common K65R drug-resistance mutation, had impaired replication capacity and higher fidelity. These results have implications for the pathogenesis of drug-resistant HIV.

The dinucleotide CpG is highly underrepresented in the genome of human immunodeficiency virus type 1 (HIV-1). To identify the source of CpG depletion in the HIV-1 genome, we investigated two biological mechanisms: (1) CpG methylation-induced transcriptional silencing and (2) CpG recognition by Toll-like receptors (TLRs). We hypothesized that HIV-1 has been under selective evolutionary pressure by these mechanisms leading to the reduction of CpG in its genome. A CpG depleted genome would enable HIV-1 to avoid methylation-induced transcriptional silencing and/or to avoid recognition by TLRs that identify foreign CpG sequences. We investigated these two hypotheses by determining the sequence context dependency of CpG depletion and comparing it with that of CpG methylation and TLR recognition.We found that in both human and HIV-1 genomes the CpG motifs flanked by T/A were depleted most and those flanked by C/G were depleted least. Similarly, our analyses of human methylome data revealed that the CpG motifs flanked by T/A were methylated most and those flanked by C/G were methylated least. Given that a similar CpG depletion pattern was observed for the human genome within which CpGs are not likely to be recognized by TLRs, we argue that the main source of CpG depletion in HIV-1 is likely host-induced methylation. Analyses of CpG motifs in over 100 viruses revealed that this unique CpG representation pattern is specific to the human and simian immunodeficiency viruses.

CD8+ T cells are important for the control of chronic HIV infection. However, the virus rapidly acquires "escape mutations" that reduce CD8+ T cell recognition and viral control. The timing of when immune escape occurs at a given epitope varies widely among patients and also among different epitopes within a patient. The strength of the CD8+ T cell response, as well as mutation rates, patterns of particular amino acids undergoing escape, and growth rates of escape mutants, may affect when escape occurs. In this study, we analyze the epitope-specific CD8+ T cells in 25 SIV-infected pigtail macaques responding to three SIV epitopes. Two epitopes showed a variable escape pattern and one had a highly monomorphic escape pattern. Despite very different patterns, immune escape occurs with a similar delay of on average 18 d after the epitope-specific CD8+ T cells reach 0.5% of total CD8+ T cells. We find that the most delayed escape occurs in one of the highly variable epitopes, and that this is associated with a delay in the epitope-specific CD8+ T cells responding to this epitope. When we analyzed the kinetics of immune escape, we found that multiple escape mutants emerge simultaneously during the escape, implying that a diverse population of potential escape mutants is present during immune selection. Our results suggest that the conservation or variability of an epitope does not appear to affect the timing of immune escape in SIV. Instead, timing of escape is largely determined by the kinetics of epitope-specific CD8+ T cells.

The human genome encodes for a family of editing enzymes known as APOBEC3 (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like3). They induce context dependent G-to-A changes, referred to as "hypermutation", in the genome of viruses such as HIV, SIV, HBV and endogenous retroviruses. Hypermutation is characterized by aligning affected sequences to a reference sequence. We show that indels (insertions/deletions) in the sequences lead to an incorrect assignment of APOBEC3 targeted and non-target sites. This can result in an incorrect identification of hypermutated sequences and erroneous biological inferences made based on hypermutation analysis.

We used a multivariate data analysis approach to identify motifs associated with HIV hypermutation by different APOBEC3 enzymes. The analysis showed that APOBEC3G targets G mainly within GG, TG, TGG, GGG, TGGG and also GGGT. The G nucleotides flanked by a C at the 3′ end (in +1 and +2 positions) were indicated as disfavoured targets by APOBEC3G. The G nucleotides within GGGG were found to be targeted at a frequency much less than what is expected. We found that the infrequent G-to-A mutation within GGGG is not limited to the inaccessibility, to APOBEC3, of poly Gs in the central and 39polypurine tracts (PPTs) which remain double stranded during the HIV reverse transcription. GGGG motifs outside the PPTs were also disfavoured. The motifs GGAG and GAGG were also found to be disfavoured targets for APOBEC3. The motif-dependent mutation of G within the HIV genome by members of the APOBEC3 family other than APOBEC3G was limited to GA→AA changes. The results did not show evidence of other types of context dependent G-to-A changes in the HIV genome. © 2014 Ebrahimi et al.

UNLABELLED: The influence of major histocompatibility complex class I (MHC-I) alleles on human immunodeficiency virus (HIV) diversity in humans has been well characterized at the population level. MHC-I alleles likely affect viral diversity in the simian immunodeficiency virus (SIV)-infected pig-tailed macaque (Macaca nemestrina) model, but this is poorly characterized. We studied the evolution of SIV in pig-tailed macaques with a range of MHC-I haplotypes. SIV(mac251) genomes were amplified from the plasma of 44 pig-tailed macaques infected with SIV(mac251) at 4 to 10 months after infection and characterized by Illumina deep sequencing. MHC-I typing was performed on cellular RNA using Roche/454 pyrosequencing. MHC-I haplotypes and viral sequence polymorphisms at both individual mutations and groups of mutations spanning 10-amino-acid segments were linked using in-house bioinformatics pipelines, since cytotoxic T lymphocyte (CTL) escape can occur at different amino acids within the same epitope in different animals. The approach successfully identified 6 known CTL escape mutations within 3 Mane-A1*084-restricted epitopes. The approach also identified over 70 new SIV polymorphisms linked to a variety of MHC-I haplotypes. Using functional CD8 T cell assays, we confirmed that one of these associations, a Mane-B028 haplotype-linked mutation in Nef, corresponded to a CTL epitope. We also identified mutations associated with the Mane-B017 haplotype that were previously described to be CTL epitopes restricted by Mamu-B*017:01 in rhesus macaques. This detailed study of pig-tailed macaque MHC-I genetics and SIV polymorphisms will enable a refined level of analysis for future vaccine design and strategies for treatment of HIV infection. IMPORTANCE: Cytotoxic T lymphocytes select for virus escape mutants of HIV and SIV, and this limits the effectiveness of vaccines and immunotherapies against these viruses. Patterns of immune escape variants are similar in HIV type 1-infected