Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance

Oxygen constitutes one of the strongest factors explaining microbial taxonomic variability in deep-sea sediments. However, deep-sea microbiome studies often lack the spatial resolution to study the oxygen gradient and transition zone beyond the oxic-anoxic dichotomy, thus leaving important questions regarding the microbial response to changing conditions unanswered. Here, we use machine learning and differential abundance analysis on 184 samples from 11 sediment cores retrieved along the Arctic Mid-Ocean Ridge to study how changing oxygen concentrations (1) are predicted by the relative abundance of higher taxa and (2) influence the distribution of individual Operational Taxonomic Units. We find that some of the most abundant classes of microorganisms can be used to classify samples according to oxygen concentration. At the level of Operational Taxonomic Units, however, representatives of common classes are not differentially abundant from high-oxic to low-oxic conditions. This weakened response to changing oxygen concentration suggests that the abundance and prevalence of highly abundant OTUs may be better explained by other variables than oxygen. Our results suggest that a relatively homogeneous microbiome is recruited to the benthos, and that the microbiome then becomes more heterogeneous as oxygen drops below 25 μM. Our analytical approach takes into account the oft-ignored compositional nature of relative abundance data, and provides a framework for extracting biologically meaningful associations from datasets spanning multiple sedimentary cores.publishedVersio

Vurdering av ytinga til Sparse Multi-Block Partial Least Squares Regression-modellar ved analyse av høy-dimensjonale fenotypiske data

FTIR and Raman spectroscopy, and MALDI-TOF mass spectrometry are emerging technologies for multidimensional phenotyping of microorganisms. While FTIR and Raman both represent a full metabolic fingerprint, MALDI spectra mainly represent the microbe's ribosomal protein composition. All methods are used for microbial identification, both by the food industry and in the clinical laboratory, but direct comparison of them by integration into the same statistical model is lacking in scientific literature. To compare the three methods, we applied a Sparse MultiBlock PLSR (SMBPLSR) routine capable of analysing all data types simultaneously. We present results indicating that this SMBPLSR method can be used to establish connections between the metabolic fingerprint of FTIR and Raman spectra, and ribosomal protein expression in MALDI-TOF data, and that the method to a large extent enables identification of samples on the strain level. Furthermore, we show that the SMBPLSR method can be used to indicate how phenotypic response to varied growth temperature is ascribed to certain types of biomolecules. Finally, we present results showing that different types of phenotypic data are treated differently by the SMBPLSR method. Grouping among variables or samples in FTIR and Raman data is achieved by a different set of latent variables than in grouping in MALDI data. The sensitivity and wealth of information obtainable from the SMBPLSR method makes it a viable complement to the already existing multivariate analysis methods.FTIR- og Raman-spektroskopi, og MALDI-TOF massespektrometri, er alle framvaksande teknologiar brukt til multidimensjonal fenotyping av mikroorganismar. Medan FTIR or Raman gjev eit fullt metabolsk fingeravtrykk, er det ribosomal proteinkomposisjon som kjem til uttrykk i MALDI. Alle desse metodane brukast for å identifisera mikrober, både i matvareindustrien og i kliniske laboratorier, men ei direkte statistisk samanlikning av metodane manglar i den vitskaplege litteraturen. For å bøte på mangelen, brukte me ei Sparsomleg MultiBlokk PLSR-metode (SMBPLSR) som kunne analysera alle datatypane samstundes. Me synar fram resultat som indikerer at SMBPLSR-metoden kan nyttast til å etablera koplingar mellom metabolsk fingeravtrykk i FTIR- og Raman-spektra på den eine sida, og ribosomalt proteinuttrykk i MALDI-TOF data på den annan. SMBPLSR-metoden kan i utstrekt grad identifisera prøver på stammenivå. Vidare syner me at SMBPLSR-metoden kan brukast til å indikera korleis fenotypisk respons på ulike veksttemperaturar kan tilskrivast spesifikke typar biomolekyl. Til slutt presenterast resultat som syner at dei ulike slaga fenotypiske data handsamast svært ulikt av SMBPLSR-metoden. Grupperingar av variablar eller prøver i FTIR- og Raman-data tilskrivast heilt andre latente variablar enn tilsvarande grupperingar i MALDI-data. Følsemda til og vellet av informasjon som kan framskaffast frå SMBPLSR-metoden gjer han til eit levedyktig tilskot til allereie eksisterande multivariate analysemetodar.M-LU

Microbial community structure in Arctic lake sediments reflect variations in Holocene climate conditions

The reconstruction of past climate variability using physical and geochemical parameters from lake sedimentary records is a well-established and widely used approach. These geological records are also known to contain large and active microbial communities, believed to be responsive to their surroundings at the time of deposition, and proceed to interact intimately with their physical and chemical environment for millennia after deposition. However, less is known about the potential legacy of past climate conditions on the contemporary microbial community structure. We analysed two Holocene-length (past 10 ka BP) sediment cores from the glacier-fed Ymer Lake, located in a highly climate-sensitive region on south-eastern Greenland. By combining physical proxies, solid as well as fluid geochemistry, and microbial population profiling in a comprehensive statistical framework, we show that the microbial community structure clusters according to established lithological units, and thus captures past environmental conditions and climatic transitions. Further, comparative analyses of the two sedimentary records indicates that the manifestation of regional climate depends on local settings such as water column depth, which ultimately constrains microbial variability in the deposited sediments. The strong coupling between physical and geochemical shifts in the lake and microbial variation highlights the potential of molecular microbiological data to strengthen and refine existing sedimentological classifications of past environmental conditions and transitions. Furthermore, this coupling implies that microbially controlled transformation and partitioning of geochemical species (e.g., manganese and sulphate) in Ymer lake today is still affected by climatic conditions that prevailed thousands of years back in time

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present