Biology subjects

Lahti, L.

Publications and source records attributed to Lahti, L..

At least 19 recordsLinked to original sources

bioRxiv2026

A unified framework for batch correction and missing data handling in large-scale and single-cell mass spectrometry proteomics

Large-scale mass spectrometry (MS)-based proteomics, including single-cell proteomics, is routinely affected by technical variation arising from discrete batch effects, inter-laboratory differences and continuous signal drift during data acquisition. Current correction strategies typically address these sources of unwanted variation independently and often require either removal of proteins with missing values or imputation before correction, both of which may lead to information loss and potential amplification of technical bias. Here we present NMFBatch, a unified statistical framework that simultaneously models discrete and continuous unwanted variation in bulk and single-cell proteomics data. NMFBatch integrates non-negative matrix factorization with generalized additive modelling and directly accommodates missing values, thereby enabling both on-the-fly imputation during correction and optional post-correction imputation. Benchmarking against six batch-correction methods using multi-laboratory reference datasets and a large plasma proteomics cohort, shows that NMFBatch consistently reduces batch-associated variation while preserving biological structure under both balanced and confounded experimental designs. Application to single-cell proteomics data further showed effective reduction of TMT- and acquisition-associated variation while retaining biologically meaningful clustering. Together, these results establish NMFBatch as a flexible framework for modelling unwanted variation in proteomics experiments, with potential applications in cross-cohort harmonization and integrative proteomics analysis. Graphical AbstractCreated in BioRender. Youssef, A. (2026) https://BioRender.com/c1q1yxt O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/726178v2_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@2b7cd1org.highwire.dtl.DTLVardef@10fada3org.highwire.dtl.DTLVardef@50e66corg.highwire.dtl.DTLVardef@147f81c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Shared antimicrobial resistance gene families across gut microbiomes in indigenous Orang Asli communities of Peninsular Malaysia

Indigenous groups across the world have been underrepresented in the ongoing efforts to map human microbiome diversity. This study investigates the gut microbiome and resistome diversities of Indigenous Orang Asli communities in Malaysia with different lifestyles and degree of urbanisation. Healthy adult participants (>18 years old) were recruited from three Indigenous communities - Temuan (urban), Temiar (semi-urban), and Jahai (rural hunter-gatherer), as well as urban Malays. Stool samples were collected on dry ice and analysed using shotgun metagenomic sequencing. Microbial alpha diversity (Shannon) decreased but antibiotic resistance genes (ARGs) diversity increased as the degree of urbanisation in the groups increased (P < 0.05). The groups contributed to 13% of the variation observed in the microbial composition (PERMANOVA, P = 0.001), and 14.5% in resistome composition (PERMANOVA, P = 0.001). Romboutsia timonensis was significantly depleted in Jahai compared to Malay (FDR = 0.04). Shared ARGs conferring resistance to beta-lactams (cfxA), tetracyclines (tet), and macrolides (erm) were observed across all groups, irrespective of geographical location, ethnicity and lifestyle. This study provides initial characterisation of the gut microbiome and resistome of three Indigenous Orang Asli communities in Malaysia. Our findings offer foundational evidence of antimicrobial resistance patterns and underscores the need for broader inclusion of underrepresented populations in national surveillance and stewardship efforts. Impact statementOur study generated one of the first integrated microbiome-resistome analyses across an urbanisation gradient of Malaysian gut microbiome. Shotgun metagenomic profiling of hard-to-access Indigenous cohorts enabled taxonomic classification for most reads, supporting high-resolution microbial community and ARG analysis. Increasing urbanisation was associated with reduced microbial alpha diversity alongside increased ARG diversity, with population grouping explaining an insightful proportion of both microbiome and resistome variation. Core resistance determinants to beta-lactams (cfxA), tetracyclines (tet), and macrolides (erm) were consistently detected across lifestyles and geographies. These results establish a baseline framework for urbanisation-linked microbial shifts in the under-studied Malaysian gut microbiome and provide actionable direction for investigating national AMR drivers, surveillance design, and targeted stewardship strategies in underrepresented populations. Data summary and code availabilitySupporting data are provided in the supplementary data files. Specific accession numbers of the sequence data are provided as Supplementary Material. Codes produced for this analysis can be found at https://zenodo.org/records/18208358.

genomics↗

bioRxiv2025

Orchestrating Microbiome Analysis with Bioconductor

The expansion of microbiome research has led to the accumulation of interlinked datasets encompassing versatile taxonomic and functional assays. The analysis of increasingly large and heterogeneous multi-modal microbiome data would benefit from unified approaches supporting the design of modular data science workflows through interoperable methods. The Bioconductor project has recently developed an optimized statistical programming framework for multi-assay data integration. Building on this foundation, we introduce a community-developed open source ecosystem for microbiome data science. In contrast to the previous alternatives, the methodology is specifically designed to support joint analysis of hierarchical, interlinked, and heterogeneous multi-table datasets that are increasingly common in modern microbiome research. This data science ecosystem encompasses open data, methods, tutorials, and an active online community. These resources support standardized and reproducible data wrangling, joint analysis, and reporting. We have detailed the functionality and usage in the online book https://microbiome.github.io/OMA, which offers guidance for prospective users and contributors.

bioinformatics↗

bioRxiv2025

Faecal transplantation from exuberant toddlers increases exploratory behaviour in rats

BackgroundBehavioural phenotypes have previously been transferred via faecal microbiota transplantation (FMT) from patients with psychiatric disorders to rodents. Studies indicate that the gut microbiota composition may be linked to certain temperament traits, defined as biologically-based differences in emotional reactivity and self-regulation. Here, we aimed to determine if the gut microbiota plays a role in temperament using an FMT approach. We focused on the temperament traits of exuberance, defined as positive reactivity, decreased behavioural inhibition, and high behavioural approach tendencies. MethodsFaeces from 2.5-year-old toddlers from FinnBrain Birth Cohort Study with high exuberance/approach or high behavioural inhibition in the LabTAB bubbles-episode was transferred to juvenile male Sprague Dawley rats (age 22/23 days). Behaviour of the rat recipients (n=53) was assessed using the novel non-social arena, novel social arena, hole board test for exploratory behaviour, social approach-avoidance test, and forced swim test. The faecal pellets collected from the rodents were analyzed with 16s rRNA sequencing and faecal samples from the sample of toddlers (which included the donors, n=176) were analysed using short-read metagenomic sequencing. The striatum and prefrontal cortex from the rodents brains were analysed post-mortem using RNAseq. ResultsMicrobiome from toddlers with high exuberance traits induced increased exploratory behaviour compared to vehicle-controls and rats receiving faeces from inhibited toddlers. Locomotor activity, social, and depressive-like behaviour remained unaffected. We noted a downregulation of the dopamine synapse pathway within the striatum of the rats that received faeces from the inhibited trait donors compared with vehicle-controls. Faecal microbiota of rats receiving faeces from the same donor resembled more each other than rats from a different cage. Clostridium species AM29 11AC in toddler microbiome was positively related to exuberance, but there were no cross-sectional associations between faecal metabolites in the human sample. ConclusionsFMT from exuberant toddlers lead to altered exploratory-related behaviour in rats.

microbiology↗

bioRxiv2025

Multi-omics time-series analysis in microbiome research: a systematic review

Recent developments in data generation have opened up unprecedented insights into living systems. It has been recognized that integrating and characterizing temporal variation simultaneously across multiple scales, from specific molecular interactions to entire ecosystems, is crucial for uncovering biological mechanisms and understanding the emergence of complex phenotypes. With the increasing number of studies incorporating multi-omics data sampled over time, it has become clear that integrated approaches are pivotal for these efforts. However, standard data analytical practices in longitudinal multi-omics are still shaping up and many of the available methods have not yet been widely evaluated and adopted. Thus, we performed a systematic literature review on data science methods for longitudinal multi-omics, with a particular focus on microbiome research.

bioinformatics↗

bioRxiv2025

Biome-specific genome catalogues reveal functional potential of shallow sequencing

The use of 16S rRNA metabarcoding for functional prediction is limited by several biases. Shallow shotgun sequencing is a cost-effective and taxonomically high-resolution alternative to 16S rRNA metabarcoding, but the low sequencing depth limits functional inference. Our BioSIFTR tool maps shallow shotgun sequencing reads against single-biome databases and extrapolates into their precalculated functional profiles. We used three datasets from red junglefowl, mice, and human gut, containing matched deep shotgun metagenomic and 16S rRNA metabarcoding data for taxonomic and functional benchmarking. An additional human gut deep shotgun sequencing data set was subsampled to 1 M reads and analysed with BioSIFTR to replicate previously obtained results. BioSIFTR taxonomic and functional profiles closely agree with the results of the full deep sequencing data in all biomes. We also replicated differences in the human gut microbiome between high and low trimethylamine N-oxide producing participants, using only < 2 % of the original deep sequencing data. The BioSIFTR tool is a powerful approach which approximates the functional information of a deep-sequenced metagenome while using only a fraction of the data. Shallow shotgun sequencing combined with BioSIFTR could be a stand-in replacement for 16S rRNA metabarcoding with an increased taxonomic and functional resolution, and lower bias. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/659887v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@8ca365org.highwire.dtl.DTLVardef@13b466dorg.highwire.dtl.DTLVardef@8cad19org.highwire.dtl.DTLVardef@1b853fa_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Prebiotic xylo-oligosaccharides for alleviation of hepatic steatosis: Results from a four-month dietary intervention and determinants of response

A common complexity from increasing rates of overweight is metabolic dysfunction associated steatotic liver disease (MASLD) which can develop into more severe conditions such as fibrosis or hepatocellular carcinoma. The gut and its microbial flora interact with the liver through the gut-liver axis and supporting healthy composition and metabolism of the microbiota can benefit metabolic health. We have previously shown in animals that a xylo-oligosaccharide (XOS) prebiotic can drive beneficial gut flora and ameliorate hepatic steatosis- our current objective is to validate these results in humans. Forty-two adults (mean age 53.7, mean BMI 33.5) ingested a prebiotic dose of XOS daily for four months. Liver fat was quantified with MRI and body composition was measured. Standard clinical measurements and untargeted metabolomes were analyzed from serum. Microbiota composition, metagenome and metabolites were analyzed from fecal samples. A subgroup analysis was conducted between non-steatotic individuals, non-responders, and responders. The XOS supplementation caused decreases in detrimental amino acid degradation products (AADPs) isovalerate, isobutyrate, and phenylacetate in the gut while serum metabolome remained stable. A decrease in liver fat during the prebiotic was accompanied by a decrease in visceral fat. In a prediction analysis, this response was driven by higher initial ratio of Bacteroides to Faecalibacterium, higher fecal AADPs, and higher serum amino acids. Non-responders had metabolomic signatures of advanced steatosis, possibly impeding effects from XOS. In overweight individuals with early hepatic steatosis, XOS may correct fermentation imbalances in the gut and decrease production of harmful metabolites, which may benefit hepatic health. IMPORTANCEPre- and probiotics are emerging, yet unknown, avenues of treatment for metabolism-associated steatotic liver disease. In overweight humans, a xylo-oligosaccharide (XOS) prebiotic induced shifts in gut bacteria and their metabolites. High proteolytic activity in the gut was predictive of hepatic response to XOS, which may serve the development of personalized treatment of fatty liver.

systems biology↗

bioRxiv2025

LimROTS: A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Analysis of Proteomics Data

MotivationDifferential expression analysis plays a vital role in omics research enabling precise identification of features that associate with different phenotypes. This process is critical for uncovering biological differences between conditions, such as disease versus healthy states. In proteomics, several statistical methods have been used, ranging from simple t-tests to more advanced methods like limma and ROTS. However, a flexible method for reproducibility-optimized statistics tailored for clinical omics data has been lacking. ResultsIn this study, we developed LimROTS, a hybrid method integrating the linear model and empirical Bayes method from the limma framework with the Reproducibility-Optimized Statistics from ROTS, to create a novel moderated ranking statistic, for robust and flexible analysis of proteomics data. We validated its performance using twenty-one proteomics gold standard spike-in datasets with different protein mixtures, MS instruments, and techniques for benchmarking. This hybrid approach improves accuracy and reproducibility of complex proteomics data, making LimROTS a powerful tool for high-dimensional omics data analysis. Availability and ImplementationLimROTS has been implemented as an R/Bioconductor package, available at https://bioconductor.org/packages/LimROTS/. Additionally, the code used in this study is available in GitHub repository https://github.com/AliYoussef96/LimROTSmanuscript Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/636801v2_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@1e6dd66org.highwire.dtl.DTLVardef@1d1589eorg.highwire.dtl.DTLVardef@110f73corg.highwire.dtl.DTLVardef@d78ef2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Sox9 and Nfi transcription factors regulate the timing of neurogenesis and ependymal maturation in dopamine progenitors

Correct timing of neurogenesis is critical for both generating the correct number and subtypes of glia and neurons in the embryo, as well as preventing tumours and the depletion of stem cell pools in the adults. Here we analyse how the midbrain dopamine neuron (mDA) progenitors transition into cell cycle arrest (G0) and begin to mature into ependymal cells. The comparison of mDA progenitors from different embryonic stages revealed the upregulation of Nfi and Sox9 transcription factors during development. Their conditional inactivation in the early embryonic midbrain leads to delayed G0 entry and ependymal maturation, reduced gliogenesis, and increased generation of neurons, including mDA neurons. In contrast, their inactivation in late embryogenesis does not result in mitotic re-entry, suggesting that these factors are necessary for the G0 induction, but not for its maintenance. Our characterisation of mDA-progenitor-derived adult ependymal cells by single-cell RNA sequencing and histology show that they both retain several progenitor features but also secrete neuropeptides and contact neighbouring cells and blood vessels, indicating that these cells may form a novel part of the circumventricular organ system.

developmental biology↗

bioRxiv2024

Fecal microbiota and metabolite composition associates with stool consistency in young children

Transit time, fluid intake, diet, and overall gut health influence stool consistency. However, the relationships between the gut metabolome (microbial metabolites) and stool consistency in infants and young children remain poorly understood. Here, we analyzed the metabolome and microbiota of 618 stool samples from children aged 2.5 months (n = 360), 6 months (n = 229), 14 months (n = 274), and 30 months (n = 169) from the FinnBrain Birth Cohort Study, and related these data to stool water content and parent-reported stool consistency. Breastfeeding showed the strongest association with both stool consistency and fecal water content. Concentrations of newly identified microbial bile acid amidates were associated with constipation and stool water content, while bile salt hydrolase, an enzyme involved in bile acid deconjugation and conjugation was predicted to be negatively associated with stool water content. In addition, short-chain fatty acids, particularly acetate, were positively associated with stool water content, whereas branched-chain short-chain fatty acids showed negative associations. These findings suggest that longer gut transit time permits more extensive microbial metabolism, including the transformation of bile acid amidates. We also found that microbial taxonomic richness, diversity, and community composition were primarily associated with stool water content, with only weak associations with stool consistency. Overall, our results highlight the importance of documenting stool consistency in fecal metabolomics and microbiome research, and provide new insights into how breastfeeding, microbial metabolism, and gut transit time shape early-life gut development.

systems biology↗

bioRxiv2023

Skin microbiota variation in Indian families

BackgroundIn Indian culture, extended families have symbolized our tradition. Families often encompass members spanning multiple generations cohabiting the same household, thereby sharing ethnicity, genetics, dietary habits, lifestyles, and living conditions. The joint or extended family setup provides an opportunity to compare variations in microbiota composition within and between families. While previous research has demonstrated that skin microbiota can be influenced by factors such as ethnicity, geography, diet, age, and sex, its associations among Indian family members that may share also genetic background remains largely unexplored. MethodsThe present study involved seventy-two individuals from fifteen families in two geographical regions of Maharashtra, India. Bacterial DNA was extracted from axillary sweat samples, followed by sequencing of V3-V4 regions of the 16S rRNA. The generated taxonomic profiles were used to quantify microbiota diversity and similarities in skin microbiota composition within and between families, taking into account factors such as genetic relatedness, diet, sex, age, geographical location, and co-habitation. ResultsThe skin microbiota composition typically comprised Firmicutes, Proteobacteria, and Actinobacteria phyla. Notably, the Shannon alpha diversity was moderately associated with dietary habits and geographical location (Kruskal-Wallis; FDR<0.1), whereas no significant differences were observed for other key factors such as age, location, or sex. A significant association was also observed between taxonomic composition and shared familial membership (p=0.001; PERMANOVA), with a borderline significant association with geographical location (p=0.07). When within and between family comparisons were investigated across three generation (G1-G2, G2-G3 and G1-G3), no significant differences were observed, however, in general skin microbiota was more similar within than between families. ConclusionThis study underscores the diversity and commonalities in skin microbiota composition within and between families. We observed that every family has a unique skin microbiota and among the various covariates, significant association was observed for diet and geographical location. Our study highlights that family relations may have specific associations with skin microbiota composition and diversity. Further studies with larger sample sizes will help to elucidate the relative contributions of shared co-habitation and genetic backgrounds.

microbiology↗

bioRxiv2023

Transcriptomic atlas of midbrain dopamine neurons uncovers differential vulnerability in a Parkinsonism lesion model

Midbrain dopamine (mDA) neurons comprise diverse cells with unique innervation targets and functions. This is illustrated by the selective sensitivity of mDA neurons of the substantia nigra compacta (SNc) in patients with Parkinsons disease, while those in the ventral tegmental area (VTA) are relatively spared. Here we used single nuclei RNA sequencing (snRNA-seq) of approximately 70,000 mouse midbrain cells to build a high-resolution atlas of mouse mDA neuron diversity at the molecular level. The results showed that differences between mDA neuron groups could best be understood as a continuum without sharp differences between subtypes. Thus, we assigned mDA neurons to several "territories" and "neighborhoods" within a shifting gene expression landscape where boundaries are gradual rather than discrete. Based on the enriched gene expression patterns of these territories and neighborhoods, we were able to localize them in the adult mouse midbrain. Moreover, because the underlying mechanisms for the variable sensitivities of diverse mDA neurons to pathological insults are not well understood, we analyzed surviving neurons after partial 6-hydroxydopamine (6-OHDA) lesions to unravel gene expression patterns that correlate with mDA neuron vulnerability and resilience. Together, this atlas provides a basis for further studies on the neurophysiological role of mDA neurons in health and disease.

neuroscience↗

bioRxiv2023

Infant Left Amygdala Volume Is Negatively Associated with Fecal Microbiota Diversity

IntroductionRodent studies have addressed the importance of early life gut microbiota in the development of emotional and social functioning. Studies in human infants are still scarce, but associations with cognition and temperament have been reported. Neuroimaging studies have linked the amygdala with fecal microbiota diversity in infants, but crucially these studies have not covered the neonatal period, and the current study addressed this gap. MethodsThe study population included 65 infants drawn from the ongoing, general population based FinnBrain Birth Cohort Study. Brain MRI was performed around the age of one month (mean age 25 days). Fecal microbiota profiles (mean 68 days) were assessed by 16s rRNA amplicon sequencing at the age of 2.5 months. ResultsWe found a negative association between infant left amygdala volume and alpha diversity (n=52, beta =-0.0043, p=0.034, adjusted for infant sex, breastfeeding, delivery mode, age during fecal sampling, age from conception during scan, and intracranial volume, Fig.1). Amygdala volumes were not associated with beta diversity (p=0.21), nor with the abundances of individual genera when adjusted for the same covariates and multiple testing. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/537273v1_fig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@bf1304org.highwire.dtl.DTLVardef@798edeorg.highwire.dtl.DTLVardef@92c040org.highwire.dtl.DTLVardef@8bc2a9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure. 1.C_FLOATNO Gut microbiota alpha diversity associates negatively with left (A), but not with right (B) amygdala volume. The grey areas depict 95% confidence intervals. C_FIG ConclusionOur results provide first evidence for associations between the brain and fecal microbiota in human neonates. Although the reported data do not allow investigation of underlying mechanisms, i.e. about the directionality of the hypothesized gut-brain connection, the reported connection encourages for future investigations of manifestations of gut-brain axis in early life.

neuroscience↗

bioRxiv2022

Impacts of maternal microbiota and microbial metabolites on fetal intestine, brain and placenta

The maternal microbiota modulates fetal development, but the mechanisms of these earliest host-microbe interactions are unclear. We compared full-term fetuses from germ-free (GF) and normally colonized mouse dams by gene expression profiling and non-targeted metabolomics. The developing immune system was strongly dependent on the maternal microbial status. In the fetal intestine, critical components mediating host-microbe interactions were differentially expressed. In fetal brain and placenta, interferon and inflammatory signaling were downregulated in germ-free fetuses. Neural system development and function, translation and RNA metabolism, and regulation of energy metabolism were significantly affected at the gene expression level. These impacts were strongly associated with microbial metabolite concentrations in the fetal tissues, suggesting that they are largely, although perhaps not exclusively mediated by maternal microbial metabolites absorbed through placenta. Several aryl sulfates were among the compounds strongly associated with gene expression differences. The germ-free fetus may suffer from depletion of queuine, a bacterial hypermodified nucleobase essential for eukaryotic tRNA stability and function.

microbiology↗

bioRxiv2022

Comparison of missing data handling methods for variant pathogenicity predictors

1BackgroundModern clinical genetic tests utilize next-generation sequencing (NGS) approaches to comprehensively analyze genetic variants from patients. Out of these millions of variants, clinically relevant variants that match the patients phenotype need to be identified accurately within a rapid timeframe that facilitates clinical action. As manual evaluation of variants is not a feasible option for meeting the speed and volume requirements of clinical genetic testing, automated solutions are needed. Various machine learning (ML), artificial intelligence (AI), and in silico variant pathogenicity predictors have been developed to solve this challenge. These solutions rely on the comprehensiveness of the available data and struggle with the sparse nature of genetic variant data. Therefore, careful treatment of missing data is necessary, and the selected methods may have a huge impact on the accuracy, reliability, speed and associated computational costs. ResultsWe present an open-source framework called AMISS that can be used to evaluate performance of different methods for handling missing genetic variant data in the context of variant pathogenicity prediction. Using AMISS, we evaluated 14 methods for handling missing values. The performance of these methods varied substantially in terms of precision, computational costs, and other attributes. Overall, simpler imputation methods and specifically mean imputation performed best. ConclusionsSelection of the missing data handling method is crucial for AI/ML-based classification of genetic variants. We show that utilizing sophisticated imputation methods is not worth the cost when used in the context of genetic variant pathogenicity classification.

bioinformatics↗

bioRxiv2022

Gata2, Nkx2-2 and Skor2 form a transcription factor network regulating development of amidbrain GABAergic neuron subtype with characteristics of REM sleep regulatory neurons

The midbrain reticular formation is a mosaic of diverse GABAergic and glutamatergic neurons that have been associated with a variety of functions, including the regulation of sleep. However the molecular characteristics and development of the midbrain reticular formation neurons are poorly understood. As the transcription factor Gata2 is required for the development of all GABAergic neurons derived from the embryonic mouse midbrain, we hypothesized that the genes expressed downstream of Gata2 could contribute to the diversification of GABAergic neuron subtypes in this brain region. Here, we show that Gata2 is indeed required for the expression of several lineage-specific transcription factors in post-mitotic midbrain GABAergic neuron precursors. These include a homeodomain transcription factor Nkx2-2 and a SKI family transcriptional repressor Skor2, which are co-expressed in a restricted group of GABAergic precursors in the midbrain reticular formation. Both Gata2, and Nkx2-2 function is required for the expression of Skor2 in GABAergic precursors. In the adult mouse as well as rat midbrain, the Nkx2-2 and Skor2 expressing GABAergic neurons locate at the boundary of the ventrolateral periaqueductal gray and the midbrain reticular formation, an area shown to contain REM-off neurons regulating REM sleep. In addition to the characteristic localization, the Skor2 positive cells increase their activity upon REM sleep inhibition, send projections to a pontine region associated with sleep control and are responsive to orexins, consistent with the known properties of the midbrain REM-off neurons.

developmental biology↗

bioRxiv2021

Quantifying the impact of ecological memory on the dynamics of interacting communities

Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications. Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the outcomes of a well-characterized interaction model are affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity. Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory on ecological communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions. Author summaryAn ecosystem is said to exhibit ecological memory when its future states do not only depend on its current state but also on its initial state and trajectory. Memory may arise through various mechanisms as organisms adapt to their environment, modify it, and accumulate biotic and abiotic material. It may also emerge from phenotypic heterogeneity at the population level. Despite its commonness in nature, ecological memory and its potential influence on ecosystem dynamics have been so far overlooked in many applied contexts. Here, we use modeling to investigate how memory can influence the dynamics, composition, and stability landscape of ecological communities. We incorporate long-term memory effects into a multi-species model recently introduced to investigate alternative stable states in microbial communities. We assess the impact of memory on key aspects of model behavior and validate our findings using a model parameterized by empirical data from the human gut microbiota. Our approach for modeling memory and studying its implications has the potential to improve our understanding of microbial community dynamics and ultimately our ability to predict, manipulate, and experimentally design microbial ecosystems. It could also be applied more broadly in the study of systems composed of interacting components.

systems biology↗

bioRxiv2021

OGUs enable effective, phylogeny-aware analysis of even shallow metagenome community structures

We introduce Operational Genomic Unit (OGU), a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent from taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldomly applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in one synthetic and two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome datasets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project dataset, and more accurate prediction of human age by the gut microbiomes in the Finnish population. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate OGU adoption in future metagenomics studies. ImportanceShotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. However, current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution compared to 16S rRNA amplicon sequence variant analysis. To solve these challenges, we introduce Operational Genomic Units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition while (ii) permitting use of phylogeny-aware tools. Our analysis of real-world datasets shows several advantages over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGU as standard practice in metagenomic studies.

bioinformatics↗