The history of human populations in Africa is complex and includes various demographic events that influenced patterns of genetic variation across the continent. Through genetic studies of modern-day, and most recently, ancient African genetic variation, it became evident that deep African history is captured by the relationships among hunter-gatherers. Furthermore, it was shown that agriculture had a large influence on the distribution of current-day Africans. These later population movements changed the demographic face of the continent and descendants of farming groups today form the majority populations across Africa. Ancient DNA methods are continually evolving, and we see evidence of this in how research has advanced in the last decade. With the increased availability of full genomic data from diverse sets of modern-day and prehistoric Africans we now have more power to infer human demography. Future ancient DNA research promises to reveal more detailed stories of human prehistory in Africa. <<<

Anti-PD-1-based immunotherapy has had a major impact on cancer treatment but has only benefited a subset of patients. Among the variables that could contribute to interpatient heterogeneity is differential composition of the patients' microbiome, which has been shown to affect antitumor immunity and immunotherapy efficacy in preclinical mouse models. We analyzed baseline stool samples from metastatic melanoma patients before immunotherapy treatment, through an integration of 16 ribosomal RNA gene sequencing, metagenomic shotgun sequencing, and quantitative polymerase chain reaction for selected bacteria. A significant association was observed between commensal microbial composition and clinical response. Bacterial species more abundant in responders included , , and Reconstitution of germ-free mice with fecal material from responding patients could lead to improved tumor control, augmented T cell responses, and greater efficacy of anti-PD-L1 therapy. Our results suggest that the commensal microbiome may have a mechanistic impact on antitumor immunity in human cancer patients. <<<

Xiangdong Pu, Zhen Li, Ya Tian, Ranran Gao, Lijun Hao, Yating Hu, Chunnian He, Wei Sun, Meimei Xu, Reuben J Peters, Yves Van de Peer, Zhichao Xu, Jingyuan Song <<<

Lonicera japonica is a widespread member of the Caprifoliaceae (honeysuckle) family utilized in traditional medical practices. This twining vine honeysuckle also is a much-sought ornamental, in part due to its dynamic flower coloration, which changes from white to gold during development. The molecular mechanism underlying dynamic flower coloration in L. japonica was elucidated by integrating whole genome sequencing, transcriptomic analysis and biochemical assays. Here, we report a chromosome-level genome assembly of L. japonica, comprising nine pseudochromosomes with a total size of 843.2 Mb. We also provide evidence for a whole-genome duplication event in the lineage leading to L. japonica, which occurred after its divergence from Dipsacales and Asterales. Moreover, gene expression analysis not only revealed correlated expression of the relevant biosynthetic genes with carotenoid accumulation, but also suggested a role for carotenoid degradation in L. japonica's dynamic flower coloration. The variation of flower color is consistent with not only the observed carotenoid accumulation pattern, but also with the release of volatile apocarotenoids that presumably serve as pollinator attractants. Beyond novel insights into the evolution and dynamics of flower coloration, the high-quality L. japonica genome sequence also provides a foundation for molecular breeding to improve desired characteristics. <<<

The aim of this paper is to provide a comprehensive review of statistical challenges in neuroimaging data analysis from neuroimaging techniques to large-scale neuroimaging studies to statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate the four common themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four common themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges. <<<

Tao Zhong , Jingkuan Wei , Kunhua Wu , Liangjun Chen , Fenqiang Zhao , Yuchen Pei , Ya Wang , Hongjiang Zhang , Zhengwang Wu , Ying Huang , Tengfei Li , Li Wang , Yongchang Chen , Weizhi Ji , Yu Zhang , Gang Li , Yuyu Niu <<<

Longitudinal brain imaging atlases with densely sampled time-points and ancillary anatomical information are of fundamental importance in studying early developmental characteristics of human and non-human primate brains during infancy, which feature extremely dynamic imaging appearance, brain shape and size. However, for non-human primates, which are highly valuable animal models for understanding human brains, the existing brain atlases are mainly developed based on adults or adolescents, denoting a notable lack of temporally densely-sampled atlases covering the dynamic early brain development. To fill this critical gap, in this paper, we construct a comprehensive set of longitudinal brain atlases and associated tissue probability maps (gray matter, white matter, and cerebrospinal fluid) with totally 12 time-points from birth to 4 years of age (i.e., 1, 2, 3, 4, 5, 6, 9, 12, 18, 24, 36, and 48 months of age) based on 175 longitudinal structural MRI scans from 39 typically-developing cynomolgus macaques, by leveraging state-of-the-art computational techniques tailored for early developing brains. Furthermore, to facilitate region-based analysis using our atlases, we also provide two popular hierarchy parcellations, i.e., cortical hierarchy maps (6 levels) and subcortical hierarchy maps (6 levels), on our longitudinal macaque brain atlases. These early developing atlases, which have the densest time-points during infancy (to the best of our knowledge), will greatly facilitate the studies of macaque brain development. <<<

The hallmarks of cancer conceptualization is a heuristic tool for distilling the vast complexity of cancer phenotypes and genotypes into a provisional set of underlying principles. As knowledge of cancer mechanisms has progressed, other facets of the disease have emerged as potential refinements. Herein, the prospect is raised that phenotypic plasticity and disrupted differentiation is a discrete hallmark capability, and that nonmutational epigenetic reprogramming and polymorphic microbiomes both constitute distinctive enabling characteristics that facilitate the acquisition of hallmark capabilities. Additionally, senescent cells, of varying origins, may be added to the roster of functionally important cell types in the tumor microenvironment. SIGNIFICANCE: Cancer is daunting in the breadth and scope of its diversity, spanning genetics, cell and tissue biology, pathology, and response to therapy. Ever more powerful experimental and computational tools and technologies are providing an avalanche of "big data" about the myriad manifestations of the diseases that cancer encompasses. The integrative concept embodied in the hallmarks of cancer is helping to distill this complexity into an increasingly logical science, and the provisional new dimensions presented in this perspective may add value to that endeavor, to more fully understand mechanisms of cancer development and malignant progression, and apply that knowledge to cancer medicine. <<<

Faces of different people elicit distinct fMRI patterns in several face-selective regions of the human brain. Here we used representational similarity analysis to investigate what type of identity-distinguishing information is encoded in three face-selective regions: fusiform face area (FFA), occipital face area (OFA), and posterior superior temporal sulcus (pSTS). In a sample of 30 human participants (22 females, 8 males), we used fMRI to measure brain activity patterns elicited by naturalistic videos of famous face identities, and compared their representational distances in each region with models of the differences between identities. We built diverse candidate models, ranging from low-level image-computable properties (pixel-wise, GIST, and Gabor-Jet dissimilarities), through higher-level image-computable descriptions (OpenFace deep neural network, trained to cluster faces by identity), to complex human-rated properties (perceived similarity, social traits, and gender). We found marked differences in the information represented by the FFA and OFA. Dissimilarities between face identities in FFA were accounted for by differences in perceived similarity, Social Traits, Gender, and by the OpenFace network. In contrast, representational distances in OFA were mainly driven by differences in low-level image-based properties (pixel-wise and Gabor-Jet dissimilarities). Our results suggest that, although FFA and OFA can both discriminate between identities, the FFA representation is further removed from the image, encoding higher-level perceptual and social face information. Recent studies using fMRI have shown that several face-responsive brain regions can distinguish between different face identities. It is however unclear whether these different face-responsive regions distinguish between identities in similar or different ways. We used representational similarity analysis to investigate the computations within three brain regions in response to naturalistically varying videos of face identities. Our results revealed that two regions, the fusiform face area and the occipital face area, encode distinct identity information about faces. Although identity can be decoded from both regions, identity representations in fusiform face area primarily contained information about social traits, gender, and high-level visual features, whereas occipital face area primarily represented lower-level image features. <<<

Nazli Dizman, Luis Meza, Paulo Bergerot, Marice Alcantara, Tanya Dorff, Yung Lyou, Paul Frankel, Yujie Cui, Valerie Mira, Marian Llamas, Joann Hsu, Zeynep Zengin, Nicholas Salgia, Sabrina Salgia, Jasnoor Malhotra, Neal Chawla, Alex Chehrazi-Raffle, Ramya Muddasani, John Gillece, Lauren Reining, Jeff Trent, Motomichi Takahashi, Kentaro Oka, Seiya Higashi, Marcin Kortylewski, Sarah K Highlander, Sumanta K Pal <<<

Previous studies have suggested that the gut microbiome influences the response to checkpoint inhibitors (CPIs) in patients with cancer. CBM588 is a bifidogenic live bacterial product that we postulated could augment CPI response through modulation of the gut microbiome. In this open-label, single-center study (NCT03829111), 30 treatment-naive patients with metastatic renal cell carcinoma with clear cell and/or sarcomatoid histology and intermediate- or poor-risk disease were randomized 2:1 to receive nivolumab and ipilimumab with or without daily oral CBM588, respectively. Stool metagenomic sequencing was performed at multiple timepoints. The primary endpoint to compare the relative abundance of Bifidobacterium spp. at baseline and at 12 weeks was not met, and no significant differences in Bifidobacterium spp. or Shannon index associated with the addition of CBM588 to nivolumab-ipilimumab were detected. Secondary endpoints included response rate, progression-free survival (PFS) and toxicity. PFS was significantly longer in patients receiving nivolumab-ipilimumab with CBM588 than without (12.7 months versus 2.5 months, hazard ratio 0.15, 95% confidence interval 0.05-0.47, P = 0.001). Although not statistically significant, the response rate was also higher in patients receiving CBM588 (58% versus 20%, P = 0.06). No significant difference in toxicity was observed between the study arms. The data suggest that CBM588 appears to enhance the clinical outcome in patients with metastatic renal cell carcinoma treated with nivolumab-ipilimumab. Larger studies are warranted to confirm this clinical observation and elucidate the mechanism of action and the effects on microbiome and immune compartments. <<<

Andreas Schuh , Antonios Makropoulos , Emma C. Robinson , Lucilio Cordero-Grande , Emer Hughes , Jana Hutter , Anthony N. Price , Maria Murgasova , Rui Pedro A. G. Teixeira , Nora Tusor , Johannes K. Steinweg , Suresh Victor , Mary A. Rutherford , Joseph V. Hajnal , A. David Edwards , Daniel Rueckert <<<

AbstractPremature birth increases the risk of developing neurocognitive and neurobe-havioural disorders. The mechanisms of altered brain development causing these disorders are yet unknown. Studying the morphology and function of the brain during maturation provides us not only with a better understanding of normal development, but may help us to identify causes of abnormal development and their consequences. A particular difficulty is to distinguish abnormal patterns of neurodevelopment from normal variation. The Developing Human Connectome Project (dHCP) seeks to create a detailed four-dimensional (4D) connectome of early life. This connectome may provide insights into normal as well as abnormal patterns of brain development. As part of this project, more than a thousand healthy fetal and neonatal brains will be scanned in vivo. This requires computational methods which scale well to larger data sets. We propose a novel groupwise method for the construction of a spatio-temporal model of mean morphology from cross-sectional brain scans at different gestational ages. This model scales linearly with the number of images and thus improves upon methods used to build existing public neonatal atlases, which derive correspondence between all pairs of images. By jointly estimating mean shape and longitudinal change, the atlas created with our method overcomes temporal inconsistencies, which are encountered when mean shape and intensity images are constructed separately for each time point. Using this approach, we have constructed a spatio-temporal atlas from 275 healthy neonates between 35 and 44 weeks post-menstrual age (PMA). The resulting atlas qualitatively preserves cortical details significantly better than publicly available atlases. This is moreover confirmed by a number of quantitative measures of the quality of the spatial normalisation and sharpness of the resulting template brain images. <<<

Adolescents' secrecy is intertwined with perception of parents' behaviors as acts of privacy invasion. It is currently untested, however, how this transactional process operates at the within-person level-where these causal processes take place. Dutch adolescents (n = 244, M = 13.84, 38.50% boys) reported three times on perceived parental privacy invasion and secrecy. Cross-lagged panel models (CLPM) confirmed earlier findings. Privacy invasion predicted increased secrecy, but a reverse effect was found from increased secrecy to increased privacy invasion. Controlling for confounding positive group-level associations with a novel random intercept CLPM, negative within-person associations were found. Higher levels of secrecy predicted lower levels of privacy invasive behaviors at the within-person level. These opposing findings within- versus between-persons illustrate a Simpson's paradox. <<<

Abhinav Achreja, Tao Yu, Anjali Mittal, Srinadh Choppara, Olamide Animasahun, Minal Nenwani, Fulei Wuchu, Noah Meurs, Aradhana Mohan, Jin Heon Jeon, Itisam Sarangi, Anusha Jayaraman, Sarah Owen, Reva Kulkarni, Michele Cusato, Frank Weinberg, Hye Kyong Kweon, Chitra Subramanian, Max S Wicha, Sofia D Merajver, Sunitha Nagrath, Kathleen R Cho, Analisa DiFeo, Xiongbin Lu, Deepak Nagrath <<<

Recurrent loss-of-function deletions cause frequent inactivation of tumour suppressor genes but often also involve the collateral deletion of essential genes in chromosomal proximity, engendering dependence on paralogues that maintain similar function. Although these paralogues are attractive anticancer targets, no methodology exists to uncover such collateral lethal genes. Here we report a framework for collateral lethal gene identification via metabolic fluxes, CLIM, and use it to reveal MTHFD2 as a collateral lethal gene in UQCR11-deleted ovarian tumours. We show that MTHFD2 has a non-canonical oxidative function to provide mitochondrial NAD, and demonstrate the regulation of systemic metabolic activity by the paralogue metabolic pathway maintaining metabolic flux compensation. This UQCR11-MTHFD2 collateral lethality is confirmed in vivo, with MTHFD2 inhibition leading to complete remission of UQCR11-deleted ovarian tumours. Using CLIM's machine learning and genome-scale metabolic flux analysis, we elucidate the broad efficacy of targeting MTHFD2 despite distinct cancer genetic profiles co-occurring with UQCR11 deletion and irrespective of stromal compositions of tumours. <<<

Species of the Enterobacter cloacae complex are widely encountered in nature, but they can act as pathogens. The biochemical and molecular studies on E. cloacae have shown genomic heterogeneity, comprising six species: Enterobacter cloacae, Enterobacter asburiae, Enterobacter hormaechei, Enterobacter kobei, Enterobacter ludwigii and Enterobacter nimipressuralis, E. cloacae and E. hormaechei are the most frequently isolated in human clinical specimens. Phenotypic identification of all species belonging to this taxon is usually difficult and not always reliable; therefore, molecular methods are often used. Although the E. cloacae complex strains are among the most common Enterobacter spp. causing nosocomial bloodstream infections in the last decade, little is known about their virulence-associated properties. By contrast, much has been published on the antibiotic-resistance features of these microorganisms. In fact, they are capable of overproducing AmpC β-lactamases by derepression of a chromosomal gene or by the acquisition of a transferable ampC gene on plasmids conferring the antibiotic resistance. Many other resistance determinants that are able to render ineffective almost all antibiotic families have been recently acquired. Most studies on antimicrobial susceptibility are focused on E. cloacae, E. hormaechei and E. asburiae; these studies reported small variations between the species, and the only significant differences had no discriminating features. <<<

Ferroptosis is a form of regulated cell death driven by the accumulation of lipid hydroperoxides. Regulation of ferroptosis might be beneficial to cancer treatment. Non-coding RNAs (ncRNAs) are a class of RNA transcripts that generally cannot encode proteins and have been demonstrated to play critical roles in regulating ferroptosis. Herein, we developed ncFO, the ncRNA-ferroptosis association database, to document the manually curated and predicted ncRNAs that are associated with ferroptosis. Collectively, ncFO contains 90 experimentally verified entries, including 46 microRNAs (miRNAs), 21 long non-coding RNAs (lncRNAs), and 17 circular RNAs (circRNAs). In addition, ncFO also incorporates two online prediction tools based on the regulation and co-expression of ncRNA and ferroptosis genes. Using default parameters, we obtained 3260 predicted entries, including 598 miRNAs and 178 lncRNAs, by regulation, as well as 2,592,661 predicted entries, including 967 miRNAs and 9632 lncRNAs, by ncRNA-ferroptosis gene co-expression in more than 8000 samples across 20 cancer types. The detailed information of each entry includes ncRNA name, disease, species, tissue, target, regulation, publication time, and PubMed identifier. ncFO also provides survival analysis and differential expression analysis for ncRNAs. In summary, ncFO offers a user-friendly platform to search and predict ferroptosis-associated ncRNAs, which might facilitate research on ferroptosis and discover potential targets for cancer treatment. ncFO can be accessed at http://www.jianglab.cn/ncFO/. <<<

Erin Rooney Riggs, Erica F Andersen, Athena M Cherry, Sibel Kantarci, Hutton Kearney, Ankita Patel, Gordana Raca, Deborah I Ritter, Sarah T South, Erik C Thorland, Daniel Pineda-Alvarez, Swaroop Aradhya, Christa Lese Martin <<<

PURPOSE: Copy-number analysis to detect disease-causing losses and gains across the genome is recommended for the evaluation of individuals with neurodevelopmental disorders and/or multiple congenital anomalies, as well as for fetuses with ultrasound abnormalities. In the decade that this analysis has been in widespread clinical use, tremendous strides have been made in understanding the effects of copy-number variants (CNVs) in both affected individuals and the general population. However, continued broad implementation of array and next-generation sequencing-based technologies will expand the types of CNVs encountered in the clinical setting, as well as our understanding of their impact on human health.METHODS: To assist clinical laboratories in the classification and reporting of CNVs, irrespective of the technology used to identify them, the American College of Medical Genetics and Genomics has developed the following professional standards in collaboration with the National Institutes of Health (NIH)-funded Clinical Genome Resource (ClinGen) project.RESULTS: This update introduces a quantitative, evidence-based scoring framework; encourages the implementation of the five-tier classification system widely used in sequence variant classification; and recommends "uncoupling" the evidence-based classification of a variant from its potential implications for a particular individual.CONCLUSION: These professional standards will guide the evaluation of constitutional CNVs and encourage consistency and transparency across clinical laboratories. <<<

Less than 5 years ago the CRISPR/Cas nuclease was first introduced into eukaryotes, shortly becoming the most efficient and widely used tool for genome engineering. For plants, efforts were centred on obtaining heritable changes in most transformable crop species by inducing mutations into open reading frames of interest, via non-homologous end joining. Now it is important to take the next steps and further develop the technology to reach its full potential. For breeding, besides using DNA-free editing and avoiding off target effects, it will be desirable to apply the system for the mutation of regulatory elements and for more complex genome rearrangements. Targeting enzymatic activities, like transcriptional regulators or DNA modifying enzymes, will be important for plant biology in the future. <<<

Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of 'big data' in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment. <<<

BACKGROUND: Insertion sequences (ISs) are mobile repeat sequences and most of them can copy themselves to new host genome locations, leading to genome plasticity and gene regulation in prokaryotes. In this study, we present functional and evolutionary relationships between IS and neighboring genes in a large-scale comparative genomic analysis.RESULTS: IS families were located in all prokaryotic phyla, with preferential occurrence of IS3, IS4, IS481, and IS5 families in Alpha-, Beta-, and Gammaproteobacteria, Actinobacteria and Firmicutes as well as in eukaryote host-associated organisms and autotrophic opportunistic pathogens. We defined the concept of the IS-Gene couple (IG), which allowed to highlight the functional and regulatory impacts of an IS on the closest gene. Genes involved in transcriptional regulation and transport activities were found overrepresented in IG. In particular, major facilitator superfamily (MFS) transporters, ATP-binding proteins and transposases raised as favorite neighboring gene functions of IS hotspots. Then, evolutionary conserved IS-Gene sets across taxonomic lineages enabled the classification of IS-gene couples into phylum, class-to-genus, and species syntenic IS-Gene couples. The IS5, IS21, IS4, IS607, IS91, ISL3 and IS200 families displayed two to four times more ISs in the phylum and/or class-to-genus syntenic IGs compared to other IS families. This indicates that those families were probably inserted earlier than others and then subjected to horizontal transfer, transposition and deletion events over time. In phylum syntenic IG category, Betaproteobacteria, Crenarchaeota, Calditrichae, Planctomycetes, Acidithiobacillia and Cyanobacteria phyla act as IS reservoirs for other phyla, and neighboring gene functions are mostly related to transcriptional regulators. Comparison of IS occurrences with predicted regulatory motifs led to ~ 26.5% of motif-containing ISs with 2 motifs per IS in average. These results, concomitantly with short IS-Gene distances, suggest that those ISs would interfere with the expression of neighboring genes and thus form strong candidates for an adaptive pairing.CONCLUSIONS: All together, our large-scale study provide new insights into the IS genetic context and strongly suggest their regulatory roles. <<<

Artificial intelligence (AI) models based on deep learning now represent the state of the art for making functional predictions in genomics research. However, the underlying basis on which predictive models make such predictions is often unknown. For genomics researchers, this missing explanatory information would frequently be of greater value than the predictions themselves, as it can enable new insights into genetic processes. We review progress in the emerging area of explainable AI (xAI), a field with the potential to empower life science researchers to gain mechanistic insights into complex deep learning models. We discuss and categorize approaches for model interpretation, including an intuitive understanding of how each approach works and their underlying assumptions and limitations in the context of typical high-throughput biological datasets. <<<

Speech enhancement is a critical component of many user-oriented audio applications, yet current systems still suffer from distorted and unnatural outputs. While generative models have shown strong potential in speech synthesis, they are still lagging behind in speech enhancement. This work leverages recent advances in diffusion probabilistic models, and proposes a novel speech enhancement algorithm that incorporates characteristics of the observed noisy speech signal into the diffusion and reverse processes. More specifically, we propose a generalized formulation of the diffusion probabilistic model named conditional diffusion probabilistic model that, in its reverse process, can adapt to non-Gaussian real noises in the estimated speech signal. In our experiments, we demonstrate strong performance of the proposed approach compared to representative generative models, and investigate the generalization capability of our models to other datasets with noise characteristics unseen during training. <<<